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Zhang Y, Wan Z, Wang D, Meng J, Ma F, Guo Y, Liu J, Li G, Liu Y. Multi-scale feature aggregation and fusion network with self-supervised multi-level perceptual loss for textures preserving low-dose CT denoising. Phys Med Biol 2024. [PMID: 38593821 DOI: 10.1088/1361-6560/ad3c91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
OBJECTIVE The textures and detailed structures in computed tomography (CT) images are highly desirable for clinical diagnosis. This study aims to expand the current body of work on textures and details preserving convolutional neural networks for low-dose CT (LDCT) image denoising task. APPROACH This study proposed a novel Multi-scale Feature Aggregation and Fusion network (MFAF-net) for LDCT image denoising. Specifically, we proposed a Multi-scale Residual Feature Aggregation Module (MRFAM) to characterize multi-scale structural information in CT images, which captures regional-specific inter-scale variations using learned weights. We further proposed a Cross-level Feature Fusion Module (CFFM) to integrate cross-level features, which adaptively weights the contributions of features from encoder to decoder by using a Spatial Pyramid Attention (SPA) mechanism. Moreover, we proposed a Self-supervised Multi-level Perceptual Loss Module (SMPLM) to generate multi-level auxiliary perceptual supervision for recovery of salient textures and structures of tissues and lesions in CT images, which takes advantage of abundant semantic information at various levels. We introduced parameters for the perceptual loss to adaptively weight the contributions of auxiliary features of different levels and we also introduced an automatic parameter tuning strategy for these parameters. MAIN RESULTS Extensive experimental studies were performed to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can achieve better performance on both fine textures preservation and noise suppression for CT image denoising task compared with other competitive CNN based methods. SIGNIFICANCE The proposed MFAF-net takes advantage of multi-scale receptive fields, cross-level features integration and self-supervised multi-level perceptual loss, enabling more effective recovering of fine textures and detailed structures of tissues and lesions in CT images.
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Affiliation(s)
- Yuanke Zhang
- School of Computer Science, Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Zhaocui Wan
- Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Dong Wang
- Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Jing Meng
- Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Fei Ma
- School of Computer Science, Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Yanfei Guo
- School of Computer Science, Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Jianlei Liu
- Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Guangshun Li
- Qufu Normal University, 80# Yantai Road, Donggang District, Rizhao, Shandong, 276826, CHINA
| | - Yang Liu
- School of biomedical engineering, Fourth Military Medical University, 169#, Changlexi Road, Xi'an, 710032, CHINA
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Niu S, Zhang M, Qiu Y, Li S, Liang L, Liu Q, Niu T, Wang J, Ma J. Evaluation of low-dose computed tomography reconstruction using spatial-radon domain total generalized variation regularization. Phys Med Biol 2024. [PMID: 38588674 DOI: 10.1088/1361-6560/ad3c0b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction (SIR) based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity. The presented PWLS-SRDTGV model can simultaneously reconstruct high-quality CT image in space domain and its corresponding projection in radon domain. An efficient split Bregman algorithm was applied to minimize the cost function of the proposed reconstruction model. Qualitative and quantitative studies were performed to evaluate the effectiveness of the PWLS-SRDTGV image reconstruction algorithm using a digital 3D XCAT phantom and an anthropomorphic torso phantom. The experimental results demonstrate that PWLS-SRDTGV algorithm achieves notable gains in noise reduction, streak artifact suppression, and edge preservation compared with competing reconstruction approaches.
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Affiliation(s)
- Shanzhou Niu
- Gannan Normal University, Shida Road, Ganzhou, 341000, CHINA
| | - Mengzhen Zhang
- Gannan Normal University, Shida Rd., Ganzhou, Jiangxi, 341000, CHINA
| | - Yang Qiu
- Southern Medical University, Shatai Rd., Guangzhou, 518107, CHINA
| | - Shuo Li
- Gannan Normal University, Shida Rd., Ganzhou, 341000, CHINA
| | - Lijing Liang
- Gannan Normal University, Shida Rd., Ganzhou, 341000, CHINA
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, 999 Xuefu Avenue, Nanchang, 330031, CHINA
| | - Tianye Niu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, A1305,Gaoke Innovation Center,Guangqiao Road,Guangming District,, Shenzhen, 518107, CHINA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Centre, 5801 Forest Park Road, Dallas, TX 75390-9183, Dallas, Texas, 75235, UNITED STATES
| | - Jianhua Ma
- Southern Medical University, Shatai Rd, Guangzhou, Guangzhou, Guangdong, 510515, CHINA
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Wang L, Meng M, Chen S, Bian Z, Zeng D, Meng D, Ma J. Semi-supervised iterative adaptive network for low-dose CT sinogram recovery. Phys Med Biol 2024; 69:085013. [PMID: 38422540 DOI: 10.1088/1361-6560/ad2ee7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/29/2024] [Indexed: 03/02/2024]
Abstract
Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.
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Affiliation(s)
- Lei Wang
- School of Future Technology, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Mingqiang Meng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- Pazhou Lab (Huangpu), Guangdong, People's Republic of China
| | - Shixuan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- Pazhou Lab (Huangpu), Guangdong, People's Republic of China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- Pazhou Lab (Huangpu), Guangdong, People's Republic of China
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangdong, People's Republic of China
| | - Deyu Meng
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
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Han M, Baek J. Direct estimation of the noise power spectrum from patient data to generate synthesized CT noise for denoising network training. Med Phys 2024; 51:1637-1652. [PMID: 38289987 DOI: 10.1002/mp.16963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/12/2023] [Accepted: 01/18/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Developing a deep-learning network for denoising low-dose CT (LDCT) images necessitates paired computed tomography (CT) images acquired at different dose levels. However, it is challenging to obtain these images from the same patient. PURPOSE In this study, we introduce a novel approach to generate CT images at different dose levels. METHODS Our method involves the direct estimation of the quantum noise power spectrum (NPS) from patient CT images without the need for prior information. By modeling the anatomical NPS using a power-law function and estimating the quantum NPS from the measured NPS after removing the anatomical NPS, we create synthesized quantum noise by applying the estimated quantum NPS as a filter to random noise. By adding synthesized noise to CT images, synthesized CT images can be generated as if these are obtained at a lower dose. This leads to the generation of paired images at different dose levels for training denoising networks. RESULTS The proposed method accurately estimates the reference quantum NPS. The denoising network trained with paired data generated using synthesized quantum noise achieves denoising performance comparable to networks trained using Mayo Clinic data, as justified by the mean-squared-error (MSE), structural similarity index (SSIM)and peak signal-to-noise ratio (PSNR) scores. CONCLUSIONS This approach offers a promising solution for LDCT image denoising network development without the need for multiple scans of the same patient at different doses.
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Affiliation(s)
- Minah Han
- Department of Artificial Intelligence, Yonsei University, Seoul, South Korea
- Bareunex Imaging Inc., Incheon, South Korea
| | - Jongduk Baek
- Department of Artificial Intelligence, Yonsei University, Seoul, South Korea
- Bareunex Imaging Inc., Incheon, South Korea
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Yu Z, Ni P, Yu H, Zuo T, Liu Y, Wang D. Effectiveness of a single low-dose computed tomography screening for lung cancer: A population-based perspective cohort study in China. Int J Cancer 2024; 154:659-669. [PMID: 37819155 DOI: 10.1002/ijc.34741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/13/2023]
Abstract
The purpose of this perspective cohort study was to evaluate the effectiveness of low-dose computed tomography (LDCT) screening for lung cancer in China. This study was conducted under the China Urban Cancer Screening Program (CanSPUC). The analysis was based on participants aged 40 to 74 years from 2012 to 2019. A total of 255 569 eligible participants were recruited in the study. Among the 58 136 participants at high risk of lung cancer, 20 346 (35.00%) had a single LDCT scan (defined as the screened group) and 37 790 (65.00%) not (defined as the non-screened group). Overall, 1162 participants were diagnosed with lung cancer at median follow-up time of 5.25 years. The screened group had the highest cumulative incidence of lung cancer and the non-screened group had the highest cumulative lung cancer mortality and all-cause cumulative mortality. We performed inverse probability weighting (IPW) to account for potential imbalances, and Cox proportional hazards model to estimate the weighted association between mortality and LDCT scans. After IPW adjusted with baseline characteristics, the lung cancer incidence density was significantly increased (37.0% increase) (HR1.37 [95%CI 1.12-1.69]), lung cancer mortality was decreased (31.0% decrease) (HR0.69 [95%CI 0.49-0.97]), and the all-cause mortality was significantly decreased (23.0% lower) (HR0.77 [95% CI 0.68-0.87]) in the screened group. In summary, a single LDCT for lung cancer screening will reduce the mortality of lung cancer and all-cause mortality in China.
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Affiliation(s)
- Zhifu Yu
- Liaoning Office for Cancer Control and Research, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Ping Ni
- Liaoning Office for Cancer Control and Research, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Huihui Yu
- Liaoning Office for Cancer Control and Research, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Tingting Zuo
- Liaoning Office for Cancer Control and Research, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Yunyong Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Danbo Wang
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
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Choi MH, Lee SW, Pak S. Low-dose versus conventional CT urography using dual-source CT with different time-current product values and the same tube voltage: image quality and diagnostic performance in various diagnoses. Br J Radiol 2024; 97:399-407. [PMID: 38308025 DOI: 10.1093/bjr/tqad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 10/05/2023] [Accepted: 11/14/2023] [Indexed: 02/04/2024] Open
Abstract
OBJECTIVES To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT. METHODS This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated. RESULTS Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P < .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P < .001). Patient's body weight was positively correlated with noise and effective radiation dose (P < .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols. CONCLUSIONS Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract. ADVANCES IN KNOWLEDGE Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases.
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Affiliation(s)
- Moon Hyung Choi
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
| | - Sheen-Woo Lee
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
| | - Seongyong Pak
- Siemens Healthineers Ltd, Seoul 06620, Republic of Korea
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Melzig C, Hartmann S, Steuwe A, Egger J, Do TD, Geisbüsch P, Kauczor HU, Rengier F, Fink MA. BMI-Adapted Double Low-Dose Dual-Source Aortic CT for Endoleak Detection after Endovascular Repair: A Prospective Intra-Individual Diagnostic Accuracy Study. Diagnostics (Basel) 2024; 14:280. [PMID: 38337796 PMCID: PMC10855180 DOI: 10.3390/diagnostics14030280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
PURPOSE To assess the diagnostic accuracy of BMI-adapted, low-radiation and low-iodine dose, dual-source aortic CT for endoleak detection in non-obese and obese patients following endovascular aortic repair. METHODS In this prospective single-center study, patients referred for follow-up CT after endovascular repair with a history of at least one standard triphasic (native, arterial and delayed phase) routine CT protocol were enrolled. Patients were divided into two groups and allocated to a BMI-adapted (group A, BMI < 30 kg/m2; group B, BMI ≥ 30 kg/m2) double low-dose CT (DLCT) protocol comprising single-energy arterial and dual-energy delayed phase series with virtual non-contrast (VNC) reconstructions. An in-patient comparison of the DLCT and routine CT protocol as reference standard was performed regarding differences in diagnostic accuracy, radiation dose, and image quality. RESULTS Seventy-five patients were included in the study (mean age 73 ± 8 years, 63 (84%) male). Endoleaks were diagnosed in 20 (26.7%) patients, 11 of 53 (20.8%) in group A and 9 of 22 (40.9%) in group B. Two radiologists achieved an overall diagnostic accuracy of 98.7% and 97.3% for endoleak detection, with 100% in group A and 95.5% and 90.9% in group B. All examinations were diagnostic. The DLCT protocol reduced the effective dose from 10.0 ± 3.6 mSv to 6.1 ± 1.5 mSv (p < 0.001) and the total iodine dose from 31.5 g to 14.5 g in group A and to 17.4 g in group B. CONCLUSION Optimized double low-dose dual-source aortic CT with VNC, arterial and delayed phase images demonstrated high diagnostic accuracy for endoleak detection and significant radiation and iodine dose reductions in both obese and non-obese patients compared to the reference standard of triple phase, standard radiation and iodine dose aortic CT.
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Affiliation(s)
- Claudius Melzig
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sibylle Hartmann
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Andrea Steuwe
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jan Egger
- Institute for AI in Medicine, University Medicine Essen, 45147 Essen, Germany
| | - Thuy D. Do
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Philipp Geisbüsch
- Department of Vascular and Endovascular Surgery, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Department of Vascular and Endovascular Surgery, Klinikum Stuttgart, Katharinenhospital, 70199 Stuttgart, Germany
| | - Hans-Ulrich Kauczor
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Fabian Rengier
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Matthias A. Fink
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
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Marcus RP, Nagy DA, Feuerriegel GC, Anhaus J, Nanz D, Sutter R. Photon-Counting Detector CT With Denoising for Imaging of the Osseous Pelvis at Low Radiation Doses: A Phantom Study. AJR Am J Roentgenol 2024; 222:e2329765. [PMID: 37646387 DOI: 10.2214/ajr.23.29765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND. Photon-counting detector (PCD) CT may allow lower radiation doses than used for conventional energy-integrating detector (EID) CT, with preserved image quality. OBJECTIVE. The purpose of this study was to compare PCD CT and EID CT, reconstructed with and without a denoising tool, in terms of image quality of the osseous pelvis in a phantom, with attention to low radiation doses. METHODS. A pelvic phantom comprising human bones in acrylic material mimicking soft tissue underwent PCD CT and EID CT at various tube potentials and radiation doses ranging from 0.05 to 5.00 mGy. Additional denoised reconstructions were generated using a commercial tool. Noise was measured in the acrylic material. Two readers performed independent qualitative assessments that entailed determining the denoised EID CT reconstruction with the lowest acceptable dose and then comparing this reference reconstruction with PCD CT reconstructions without and with denoising, using subjective Likert scales. RESULTS. Noise was lower for PCD CT than for EID CT. For instance, at 0.05 mGy and 100 kV with tin filter, noise was 38.4 HU for PCD CT versus 48.8 HU for EID CT. Denoising further reduced noise; for example, for PCD CT at 100 kV with tin filter at 0.25 mGy, noise was 19.9 HU without denoising versus 9.7 HU with denoising. For both readers, lowest acceptable dose for EID CT was 0.10 mGy (total score, 11 of 15 for both readers). Both readers somewhat agreed that PCD CT without denoising at 0.10 mGy (reflecting reference reconstruction dose) was relatively better than the reference reconstruction in terms of osseous structures, artifacts, and image quality. Both readers also somewhat agreed that denoised PCD CT reconstructions at 0.10 mGy and 0.05 mGy (reflecting matched and lower doses, respectively, with respect to reference reconstruction dose) were relatively better than the reference reconstruction for the image quality measures. CONCLUSION. PCD CT showed better-quality images than EID CT when performed at the lowest acceptable radiation dose for EID CT. PCD CT with denoising yielded better-quality images at a dose lower than lowest acceptable dose for EID CT. CLINICAL IMPACT. PCD CT with denoising could facilitate lower radiation doses for pelvic imaging.
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Affiliation(s)
- Roy P Marcus
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Daniel A Nagy
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Georg C Feuerriegel
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | | | - Daniel Nanz
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Swiss Center for Musculoskeletal Imaging, Balgrist Campus, Zurich, Switzerland
| | - Reto Sutter
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
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Hop JF, Walstra ANH, Pelgrim GJ, Xie X, Panneman NA, Schurink NW, Faby S, van Straten M, de Bock GH, Vliegenthart R, Greuter MJW. Detectability and Volumetric Accuracy of Pulmonary Nodules in Low-Dose Photon-Counting Detector Computed Tomography: An Anthropomorphic Phantom Study. Diagnostics (Basel) 2023; 13:3448. [PMID: 37998584 PMCID: PMC10669978 DOI: 10.3390/diagnostics13223448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/05/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
The aim of this phantom study was to assess the detectability and volumetric accuracy of pulmonary nodules on photon-counting detector CT (PCD-CT) at different low-dose levels compared to conventional energy-integrating detector CT (EID-CT). In-house fabricated artificial nodules of different shapes (spherical, lobulated, spiculated), sizes (2.5-10 mm and 5-1222 mm3), and densities (-330 HU and 100 HU) were randomly inserted into an anthropomorphic thorax phantom. The phantom was scanned with a low-dose chest protocol with PCD-CT and EID-CT, in which the dose with PCD-CT was lowered from 100% to 10% with respect to the EID-CT reference dose. Two blinded observers independently assessed the CT examinations of the nodules. A third observer measured the nodule volumes using commercial software. The influence of the scanner type, dose, observer, physical nodule volume, shape, and density on the detectability and volumetric accuracy was assessed by a multivariable regression analysis. In 120 CT examinations, 642 nodules were present. Observer 1 and 2 detected 367 (57%) and 289 nodules (45%), respectively. With PCD-CT and EID-CT, the nodule detectability was similar. The physical nodule volumes were underestimated by 20% (range 8-52%) with PCD-CT and 24% (range 9-52%) with EID-CT. With PCD-CT, no significant decrease in the detectability and volumetric accuracy was found at dose reductions down to 10% of the reference dose (p > 0.05). The detectability and volumetric accuracy were significantly influenced by the observer, nodule volume, and a spiculated nodule shape (p < 0.05), but not by dose, CT scanner type, and nodule density (p > 0.05). Low-dose PCD-CT demonstrates potential to detect and assess the volumes of pulmonary nodules, even with a radiation dose reduction of up to 90%.
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Affiliation(s)
- Joost F. Hop
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Anna N. H. Walstra
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Gert-Jan Pelgrim
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China;
| | - Noor A. Panneman
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Niels W. Schurink
- Siemens Healthineers Nederland B.V., 2595 BN Den Haag, The Netherlands
| | - Sebastian Faby
- Computed Tomography, Siemens Healthcare GmbH, 91301 Forchheim, Germany;
| | - Marcel van Straten
- Department of Radiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Geertruida H. de Bock
- Department of Epidemiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands;
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Marcel J. W. Greuter
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
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10
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Colamonici M, Khouzam N, Dell C, Auge-Bronersky K, Pacheco E, Rubinstein I, Recht B. Promoting lung cancer screening of high-risk patients by primary care providers. Cancer 2023; 129:3574-3581. [PMID: 37449669 DOI: 10.1002/cncr.34955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/28/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest of eligible patients remains low. Accordingly, augmentation of appropriate LCS referrals by primary care providers (PCPs) was sought. METHODS The quality improvement (QI) project was performed between April 2021 and June 2022. It incorporated patient education, shared decision-making (SDM) with PCPs, and tracking of initial LDCT completion. In each case, lag time (LT) to LCS and pack-years (PYs) were calculated from initial LCS eligibility. The cohort's scores were compared to national scores. Patient zip codes were used to create a geographic map of our cohort for comparison with public health data. RESULTS An immediate and sustained increase in weekly LCS referrals from PCPs was recorded. Of 337 initial referrals, 95% were men, consisting of 66.2% Black, 28.4% White, and 5.4% other. Mean PY was less for minorities (45.3 vs. 37.3 years; p = .0002) but mean LT was greater for Whites (7.9 vs. 6.2 years; p = .03). Twenty-five percent of veterans failed to report to their scheduled screening, and two declined referrals. Notably, most no-show patients lived in transit deserts. Furthermore, Lung-RADS scores 4B/4X were more than double the expected prevalence (p = .008). CONCLUSIONS The PCPs in this study successfully augmented LCS referrals. A substantial proportion of these patients were no-shows, and our data suggest complex racial and socioeconomic factors as contributing variables. In addition, a higher-than-expected number of initial Lung-RADS scores 4B/4X were reported. A large, multisite QI project is warranted to address overcoming potential transportation barriers in high-risk patient populations.
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Affiliation(s)
- Marco Colamonici
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Nader Khouzam
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Catherine Dell
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
| | - Kristin Auge-Bronersky
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Esther Pacheco
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Israel Rubinstein
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Bradley Recht
- Medical Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
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11
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Gan G, Gong W, Jia L, Zhang W, Wang S, Zhou J, Jiang H. Study of peripheral dose from low-dose CT to adaptive radiotherapy of postoperative prostate cancer. Front Oncol 2023; 13:1227946. [PMID: 38023166 PMCID: PMC10646313 DOI: 10.3389/fonc.2023.1227946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Objectives The increasing use of computed tomography (CT) for adaptive radiotherapy (ART) has raised concerns about the peripheral radiation dose. This study investigates the feasibility of low-dose CT (LDCT) for postoperative prostate cancer ART to reduce the peripheral radiation dose, and evaluates the peripheral radiation dose of different imaging techniques and propose an image enhancement method based on deep learning for LDCT. Materials and methods A linear accelerator integrated with a 16-slice fan-beam CT from UIH (United Imaging Healthcare, China) was utilized for prostate cancer ART. To reduce the tube current of CT for ART, LDCT was acquired. Peripheral doses of normal-dose CT (NDCT), LDCT, and mega-voltage computed tomography (MV-CT) were measured using a cylindrical Virtual Water™ phantom and an ion chamber. A deep learning model of LDCT for abdominal and pelvic-based cycle-consistent generative adversarial network was employed to enhance the image quality of LDCT. Six postoperative prostate cancer patients were selected to evaluate the feasibility of low-dose CT network restoration images (RCT) by the deep learning model for ART. The three aspects among NDCT, LDCT, and RCT were compared: the Hounsfield Unit (HU) of the tissue, the Dice Similarity Coefficient (DSC) criterion of target and organ, and dose calculation differences. Results In terms of peripheral dose, the LDCT had a surface measurement point dose of approximately 1.85 mGy at the scanning field, while the doses of NDCT and MV-CT were higher at 22.85 mGy and 29.97 mGy, respectively. However, the image quality of LDCT was worse than NDCT. When compared to LDCT, the tissue HU value of RCT showed a significant improvement and was closer to that of NDCT. The DSC results for target CTV between RCT and NDCT were also impressive, reaching up to 94% for bladder and femoral heads, 98% for rectum, and 94% for the target organ. Additionally, the dose calculation differences for the ART plan based on LDCT and NDCT were all within 1%. Overall, these findings suggest that RCT can provide an effective alternative to NDCT and MV-CT with similar or better outcomes in HU values of tissue and organ damage. More testing is required before clinical application.
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Affiliation(s)
- Guanghui Gan
- Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wei Gong
- Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lecheng Jia
- Real-time Lab, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
- Zhejiang Engineering Research Center for Innovation and Application of Intelligent Radiotherapy Technology, Wenzhou, China
| | - Wei Zhang
- Radiotherapy Business Unit, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Shimei Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Juying Zhou
- Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hua Jiang
- Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China
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12
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Matsushita Y, Yokoyama T, Noguchi T, Nakagawa T. Assessment of skeletal muscle using deep learning on low-dose CT images. Glob Health Med 2023; 5:278-284. [PMID: 37908512 PMCID: PMC10615034 DOI: 10.35772/ghm.2023.01050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/08/2023] [Accepted: 09/22/2023] [Indexed: 11/02/2023]
Abstract
The visceral fat area obtained by computed tomography (CT) at the navel level is clinically used as an indicator of visceral fat obesity in Japan. Analysis of skeletal muscle mass using CT images at the navel level may potentially support concurrent assessment of sarcopenia and sarcopenic obesity. The purpose of this study was to assess the performance of deep learning models (DLMs) for skeletal muscle mass measurement using low-dose abdominal CT. The primary dataset used in this study included 11,494 low-dose abdominal CT images at navel level acquired in 7,370 subjects for metabolic syndrome screening. The publicly available Cancer Imaging Archive (TCIA) dataset, including 5,801 abdominal CT images, was used as a complementary dataset. For abdominal CT image segmentation, we used the SegU-net DLM with different filter size and hierarchical depth. The segmentation accuracy was assessed by measuring the dice similarity coefficient (DSC), cross-sectional area (CSA) error, and Bland-Altman plots. The proposed DLM achieved a DSC of 0.992 ± 0.012, a CSA error of 0.41 ± 1.89%, and a Bland-Altman percent difference of -0.1 ± 3.8%. The proposed DLM was able to automatically segment skeletal muscle mass measurements from low-dose abdominal CT with high accuracy.
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Affiliation(s)
- Yumi Matsushita
- Department of Clinical Research, National Center for Global Health and Medicine, Tokyo, Japan
| | - Tetsuji Yokoyama
- Department of Health Promotion, National Institute of Public Health, Saitama, Japan
| | - Tomoyuki Noguchi
- Department of Radiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Toru Nakagawa
- Hitachi, Ltd. Hitachi Health Care Center, Ibaraki, Japan
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13
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Chen L, Yang X, Huang Z, Long Y, Ravishankar S. Multi-layer clustering-based residual sparsifying transform for low-dose CT image reconstruction. Med Phys 2023; 50:6096-6117. [PMID: 37535932 DOI: 10.1002/mp.16645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/03/2023] [Accepted: 07/16/2023] [Indexed: 08/05/2023] Open
Abstract
PURPOSE The recently proposed sparsifying transform (ST) models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure reveal great potential for learning features in different layers. In this study, we propose a network-structured ST learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning. The proposed MCST scheme learns multiple different unitary transforms in each layer by dividing each layer's input into several classes. We apply the MCST model to low-dose CT (LDCT) reconstruction by deploying the learned MCST model into the regularizer in penalized weighted least squares (PWLS) reconstruction. METHODS The proposed MCST model combines a multi-layer sparse representation structure with multiple clusters for the features in each layer that are modeled by a rich collection of transforms. We train the MCST model in an unsupervised manner via a block coordinate descent (BCD) algorithm. Since our method is patch-based, the training can be performed with a limited set of images. For CT image reconstruction, we devise a novel algorithm called PWLS-MCST by integrating the pre-learned MCST signal model with PWLS optimization. RESULTS We conducted LDCT reconstruction experiments on XCAT phantom data, Numerical Mayo Clinical CT dataset and "LDCT image and projection dataset" (Clinical LDCT dataset). We trained the MCST model with two (or three) layers and with five clusters in each layer. The learned transforms in the same layer showed rich features while additional information is extracted from representation residuals. Our simulation results and clinical results demonstrate that PWLS-MCST achieves better image reconstruction quality than the conventional filtered back-projection (FBP) method and PWLS with edge-preserving (EP) regularizer. It also outperformed recent advanced methods like PWLS with a learned multi-layer residual sparsifying transform (MARS) prior and PWLS with a union of learned transforms (ULTRA), especially for displaying clear edges and preserving subtle details. CONCLUSIONS In this work, a multi-layer sparse signal model with a nested network structure is proposed. We refer this novel model as the MCST model that exploits multi-layer residual maps to sparsify the underlying image and clusters the inputs in each layer for accurate sparsification. We presented a new PWLS framework with a learned MCST regularizer for LDCT reconstruction. Experimental results show that the proposed PWLS-MCST provides clearer reconstructions than several baseline methods. The code for PWLS-MCST is released at https://github.com/Xikai97/PWLS-MCST.
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Affiliation(s)
- Ling Chen
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Xikai Yang
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Zhishen Huang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Yong Long
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Saiprasad Ravishankar
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA
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14
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Liang H, Hu M, Ma Y, Yang L, Chen J, Lou L, Chen C, Xiao Y. Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review. Life (Basel) 2023; 13:1911. [PMID: 37763314 PMCID: PMC10532719 DOI: 10.3390/life13091911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/06/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVE For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy. METHOD We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively. RESULTS Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules. CONCLUSION It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research.
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Affiliation(s)
- Hailun Liang
- School of Public Administration and Policy, Renmin University of China, Beijing 100872, China; (H.L.)
| | - Meili Hu
- Department of Gynecology, Baoding Maternal and Child Health Care Hospital, Baoding 071000, China;
| | - Yuxin Ma
- School of Public Administration and Policy, Renmin University of China, Beijing 100872, China; (H.L.)
| | - Lei Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jie Chen
- School of Public Administration and Policy, Renmin University of China, Beijing 100872, China; (H.L.)
| | - Liwei Lou
- School of Statistics, Renmin University of China, Beijing 100872, China
| | - Chen Chen
- School of Public Administration and Policy, Renmin University of China, Beijing 100872, China; (H.L.)
| | - Yuan Xiao
- Blockchain Research Institute, Renmin University of China, Beijing 100872, China
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15
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Yu J, Zhang H, Zhang P, Zhu Y. Unsupervised learning-based dual-domain method for low-dose CT denoising. Phys Med Biol 2023; 68:185010. [PMID: 37567225 DOI: 10.1088/1361-6560/acefa2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/10/2023] [Indexed: 08/13/2023]
Abstract
Objective. Low-dose CT (LDCT) is an important research topic in the field of CT imaging because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep learning techniques have been widely applied in LDCT imaging and a large number of denoising methods have been proposed. However, One major challenge of supervised deep learning-based methods is the exactly geometric pairing of datasets with different doses. Therefore, the aim of this study is to develop an unsupervised learning-based LDCT imaging method to address the aforementioned challenges.Approach. In this paper, we propose an unsupervised learning-based dual-domain method for LDCT denoising, which consists of two stages: the first stage is projection domain denoising, in which the unsupervised learning method Noise2Self is applied to denoise the projection data with statistically independent and zero-mean noise. The second stage is an iterative enhancement approach, which combines the prior information obtained from the generative model with an iterative reconstruction algorithm to enhance the details of the reconstructed image.Main results. Experimental results show that our proposed method outperforms the comparison method in terms of denoising effect. Particularly, in terms of SSIM, the denoised results obtained using our method achieve the highest SSIM.Significance. In conclusion, our unsupervised learning-based method can be a promising alternative to the traditional supervised methods for LDCT imaging, especially when the availability of the labeled datasets is limited.
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Affiliation(s)
- Jie Yu
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
| | - Huitao Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- Shenzhen National Applied Mathematics Center, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China
| | - Peng Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
| | - Yining Zhu
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- Shenzhen National Applied Mathematics Center, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China
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16
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Lewis JA, Bonnet K, Schlundt DG, Byerly S, Lindsell CJ, Henschke CI, Yankelevitz DF, York SJ, Hendler F, Dittus RS, Vogus TJ, Kripalani S, Moghanaki D, Audet CM, Roumie CL, Spalluto LB. Rural barriers and facilitators of lung cancer screening program implementation in the veterans health administration: a qualitative study. Front Health Serv 2023; 3:1209720. [PMID: 37674596 PMCID: PMC10477991 DOI: 10.3389/frhs.2023.1209720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/04/2023] [Indexed: 09/08/2023]
Abstract
Introduction To assess healthcare professionals' perceptions of rural barriers and facilitators of lung cancer screening program implementation in a Veterans Health Administration (VHA) setting through a series of one-on-one interviews with healthcare team members. Methods Based on measures developed using Reach Effectiveness Adoption Implementation Maintenance (RE-AIM), we conducted a cross-sectional qualitative study consisting of one-on-one semi-structured telephone interviews with VHA healthcare team members at 10 Veterans Affairs medical centers (VAMCs) between December 2020 and September 2021. An iterative inductive and deductive approach was used for qualitative analysis of interview data, resulting in the development of a conceptual model to depict rural barriers and facilitators of lung cancer screening program implementation. Results A total of 30 interviews were completed among staff, providers, and lung cancer screening program directors and a conceptual model of rural barriers and facilitators of lung cancer screening program implementation was developed. Major themes were categorized within institutional and patient environments. Within the institutional environment, participants identified systems-level (patient communication, resource availability, workload), provider-level (attitudes and beliefs, knowledge, skills and capabilities), and external (regional and national networks, incentives) barriers to and facilitators of lung cancer screening program implementation. Within the patient environment, participants revealed patient-level (modifiable vulnerabilities) barriers and facilitators as well as ecological modifiers (community) that influence screening behavior. Discussion Understanding rural barriers to and facilitators of lung cancer screening program implementation as perceived by healthcare team members points to opportunities and approaches for improving lung cancer screening reach, implementation and effectiveness in VHA rural settings.
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Affiliation(s)
- Jennifer A. Lewis
- Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, United States
- Veterans Health Administration-Tennessee Valley Healthcare System, Medicine Service, Nashville, TN, United States
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt-Ingram Cancer Center, Nashville, TN, United States
| | - Kemberlee Bonnet
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
- Qualitative Research Core, Vanderbilt University Medical Center, Nashville, TN, United States
| | - David G. Schlundt
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
- Qualitative Research Core, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Susan Byerly
- Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, United States
- Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Christopher J. Lindsell
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Claudia I. Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, NY, New York, United States
- Veterans Health Administration—Phoenix VA Health Care System, Radiology Service, Phoenix, AZ, United States
| | - David F. Yankelevitz
- Department of Radiology, Icahn School of Medicine at Mount Sinai, NY, New York, United States
| | - Sally J. York
- Veterans Health Administration-Tennessee Valley Healthcare System, Medicine Service, Nashville, TN, United States
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt-Ingram Cancer Center, Nashville, TN, United States
| | - Fred Hendler
- Rex Robley VA Medical Center, Medicine Service, Louisville, KY, United States
| | - Robert S. Dittus
- Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, United States
- Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Timothy J. Vogus
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Owen Graduate School of Management, Vanderbilt University, Nashville, TN, United States
| | - Sunil Kripalani
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Drew Moghanaki
- Veterans Health Administration—Greater Los Angeles Veterans Affairs Medical Center, Radiation Oncology Service, Los Angeles, CA, United States
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Carolyn M. Audet
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Christianne L. Roumie
- Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, United States
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lucy B. Spalluto
- Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, United States
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt-Ingram Cancer Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
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Liao S, Mo Z, Zeng M, Wu J, Gu Y, Li G, Quan G, Lv Y, Liu L, Yang C, Wang X, Huang X, Zhang Y, Cao W, Dong Y, Wei Y, Zhou Q, Xiao Y, Zhan Y, Zhou XS, Shi F, Shen D. Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction. Cell Rep Med 2023; 4:101119. [PMID: 37467726 PMCID: PMC10394257 DOI: 10.1016/j.xcrm.2023.101119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.
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Affiliation(s)
- Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Mengsu Zeng
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Guobin Li
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Guotao Quan
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yang Lv
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Chun Yang
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xinglie Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiaoqian Huang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yang Zhang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Wenjing Cao
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yun Dong
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yongqin Xiao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yiqiang Zhan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 200122, China.
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18
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Mascalchi M, Picozzi G, Puliti D, Diciotti S, Deliperi A, Romei C, Falaschi F, Pistelli F, Grazzini M, Vannucchi L, Bisanzi S, Zappa M, Gorini G, Carozzi FM, Carrozzi L, Paci E. Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed. Diagnostics (Basel) 2023; 13:2197. [PMID: 37443590 DOI: 10.3390/diagnostics13132197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.
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Affiliation(s)
- Mario Mascalchi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Giulia Picozzi
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Donella Puliti
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47521 Cesena, Italy
| | - Annalisa Deliperi
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Chiara Romei
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Fabio Falaschi
- Radiodiagnostic Unit 2, Department of Diagnostic Imaging, Cisanello University Hospital of Pisa, 56124 Pisa, Italy
| | - Francesco Pistelli
- Pulmonary Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, 56124 Pisa, Italy
| | - Michela Grazzini
- Division of Pneumonology, San Jacopo Hospital Pistoia, 51100 Pistoia, Italy
| | - Letizia Vannucchi
- Division of Radiology, San Jacopo Hospital Pistoia, 51100 Pistoia, Italy
| | - Simonetta Bisanzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Marco Zappa
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Giuseppe Gorini
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Francesca Maria Carozzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
| | - Laura Carrozzi
- Pulmonary Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, 56124 Pisa, Italy
| | - Eugenio Paci
- Division of Epidemiology and Clinical Governance, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50100 Florence, Italy
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19
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Allio IR, Caobelli F, Popescu CE, Haaf P, Alberts I, Frey SM, Zellweger MJ. Low-dose coronary artery calcium scoring compared to the standard protocol. J Nucl Cardiol 2023; 30:1191-1198. [PMID: 36289163 PMCID: PMC10261226 DOI: 10.1007/s12350-022-03120-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/19/2022] [Indexed: 10/31/2022]
Abstract
BACKGROUND We aimed to compare coronary artery calcium scoring (CACS) with computed tomography (CT) with 80 and 120 kVp in a large patient population and to establish whether there is a difference in risk classification between the two scores. METHODS Patients with suspected CAD undergoing MPS were included. All underwent standard CACS assessment with 120-kVp tube voltage and with 80 kVp. Two datasets (low-dose and standard) were generated and compared. Risk classes (0 to 25, 25 to 50, 50 to 75, 75 to 90, and > 90%) were recorded. RESULTS 1511 patients were included (793 males, age 69 ± 9.1 years). There was a very good correlation between scores calculated with 120 and 80 kVp (R = 0.94, R2 = 0.88, P < .001), with Bland-Altman limits of agreement of - 563.5 to 871.9 and a bias of - 154.2. The proportion of patients assigned to the < 25% percentile class (P = .03) and with CACS = 0 differed between the two protocols (n = 264 vs 437, P < .001). CONCLUSION In a large patient population, despite a good correlation between CACS calculated with standard and low-dose CT, there is a systematic underestimation of CACS with the low-dose protocol. This may have an impact especially on the prognostic value of the calcium score, and the established "power of zero" may no longer be warranted if CACS is assessed with low-dose CT.
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Affiliation(s)
- Ileana Rosely Allio
- Department of Cardiology, Clinic of Cardiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Federico Caobelli
- Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Philip Haaf
- Department of Cardiology, Clinic of Cardiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Ian Alberts
- University Clinic of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Simon M Frey
- Department of Cardiology, Clinic of Cardiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Michael J Zellweger
- Department of Cardiology, Clinic of Cardiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
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20
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Liu Y, Wang C. An efficient 3D reconstruction method based on WT-TV denoising for low-dose CT images. Technol Health Care 2023; 31:463-475. [PMID: 37038798 DOI: 10.3233/thc-236040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
BACKGROUND In order to reduce the impact of CT radiation, low-dose CT is often used, but low-dose CT will bring more noise, affecting image quality and subsequent 3D reconstruction results. OBJECTIVE The study presents a reconstruction method based on wavelet transform-total variation (WT-TV) for low-dose CT. METHODS First, the low-dose CT images were denoised using WT and TV denoising methods. The WT method could preserve the features, and the TV method could preserve the edges and structures. Second, the two sets of denoised images were fused so that the features, edges, and structures could be preserved at the same time. Finally, FBP reconstruction was performed to obtain the final 3D reconstruction result. RESULTS The results show that The WT-TV method can effectively denoise low-dose CT and improve the clarity and accuracy of 3D reconstruction models. CONCLUSION Compared with other reconstruction methods, the proposed reconstruction method successfully addressed the issue of low-dose CT noising by further denoising the CT images before reconstruction. The denoising effect of low-dose CT images and the 3D reconstruction model were compared via experiments.
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21
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Yang M, Wang J, Zhang Z, Li J, Liu L. Transfer learning framework for low-dose CT reconstruction based on marginal distribution adaptation in multiscale. Med Phys 2023; 50:1450-1465. [PMID: 36321246 DOI: 10.1002/mp.16027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND With the increasing use of computed tomography (CT) in clinical practice, limiting CT radiation exposure to reduce potential cancer risks has become one of the important directions of medical imaging research. As the dose decreases, the reconstructed CT image will be severely degraded by projection noise. PURPOSE As an important method of image processing, supervised deep learning has been widely used in the restoration of low-dose CT (LDCT) in recent years. However, the normal-dose CT (NDCT) corresponding to a specific LDCT (it is regarded as the label of the LDCT, which is necessary for supervised learning) is very difficult to obtain so that the application of supervised learning methods in LDCT reconstruction is limited. It is necessary to construct a unsupervised deep learning framework for LDCT reconstruction that does not depend on paired LDCT-NDCT datasets. METHODS We presented an unsupervised learning framework for the transferring from the identity mapping to the low-dose reconstruction task, called marginal distribution adaptation in multiscale (MDAM). For NDCTs as source domain data, MDAM is an identity map with two parts: firstly, it establishes a dimensionality reduction mapping, which can obtain the same feature distribution from NDCTs and LDCTs; and then NDCTs is retrieved by reconstructing the image overview and details from the low-dimensional features. For the purpose of the feature transfer between source domain and target domain (LDCTs), we introduce the multiscale feature extraction in the MDAM, and then eliminate differences in probability distributions of these multiscale features between NDCTs and LDCTs through wavelet decomposition and domain adaptation learning. RESULTS Image quality evaluation metrics and subjective quality scores show that, as an unsupervised method, the performance of the MDAM approaches or even surpasses some state-of-the-art supervised methods. Especially, MDAM has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection. CONCLUSIONS We demonstrated that, the MDAM framework can reconstruct corresponding NDCTs from LDCTs with high accuracy, and without relying on any labeles. Moreover, it is more suitable for clinical application compared with supervised learning methods.
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Affiliation(s)
- Minghan Yang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jianye Wang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Ziheng Zhang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jie Li
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Lingling Liu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
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22
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Xiao H, Shi Z, Zou Y, Xu K, Yu X, Wen L, Liu Y, Chen H, Long H, Chen J, Liu Y, Cao S, Li C, Hu Y, Liao X, Yan S. One-off low-dose CT screening of positive nodules in lung cancer: A prospective community-based cohort study. Lung Cancer 2023; 177:1-10. [PMID: 36657367 DOI: 10.1016/j.lungcan.2023.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/22/2022] [Accepted: 01/06/2023] [Indexed: 01/09/2023]
Abstract
BACKGROUND To improve the early stage diagnosis and reduce the lung cancer (LC) mortality for positive nodule (PN) population, data on effectiveness of PN detection using one-off low-dose spiral computed tomography (LDCT) screening are needed to improve the PN management protocol. We evaluate the effectiveness of PN detection and developed a nomogram to predict LC risk for PNs. METHODS A prospective, community-based cohort study was conducted. We recruited 292,531 eligible candidates during 2012-2018. Individuals at high risk of LC based on risk assessment underwent LDCT screening and were divided into PN and non-PN groups. The effectiveness of PN detection was evaluated in LC incidence, mortality, and all-cause mortality. We performed subgroup analysis of characteristic variables for the association between PN and LC risk. A competing risk model was used to develop the nomogram. RESULTS Participants (n = 14901) underwent LDCT screening; PNs were detected in 1193 cases (8·0%). After a median follow-up of 6·1 years, 193 were diagnosed with LC (1·3%). Of these, 94 were in the PN group (8·0%). LC incidence, mortality, and all-cause mortality were significantly higher in the PN group (adjusted hazard ratios: 10.60 (7.91-14.20), 7.97 (5.20-12.20), and 1.94 (1.51-2.50), respectively). Additionally, various PN characteristics were associated with an increased probability of developing LC. The C-index value of the nomogram for predicting LC risk of PN individuals was 0·847. CONCLUSIONS The protocol of PNs management for improvement could focus on specific characteristic population and high-risk PN individuals by nomogram assessment.
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Affiliation(s)
- Haifan Xiao
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China.
| | - Zhaohui Shi
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Yanhua Zou
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Kekui Xu
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Xiaoping Yu
- The Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Lu Wen
- The Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Yang Liu
- Kaifu District Center for Disease Control and Prevention, 1440 Xifeng Road, Changsha 410005, China
| | - Haiyan Chen
- Fufong District Center for Disease Control and Prevention, 8 Huojuzhong Road, Changsha 410001, China
| | - Huajun Long
- Yuhua District Center for Disease Control and Prevention, 772 Zhongyiyi Road, Changsha 410007, China
| | - Jihuai Chen
- Yuelu District Center for Disease Control and Prevention, 1060 Dujuan Road, Changsha 410006, China
| | - Yanling Liu
- Tianxin District Center for Disease Control and Prevention, 86 Lianhua Road, Changsha 410000, China
| | - Shiyu Cao
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Can Li
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Yingyun Hu
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China
| | - Xianzhen Liao
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China.
| | - Shipeng Yan
- The Department of Cancer Prevention and Control, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha 410013, China.
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23
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Han M, Shim H, Baek J. Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser. Med Phys 2023; 50:2787-2804. [PMID: 36734478 DOI: 10.1002/mp.16263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/04/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The purpose of a convolutional neural network (CNN)-based denoiser is to increase the diagnostic accuracy of low-dose computed tomography (LDCT) imaging. To increase diagnostic accuracy, there is a need for a method that reflects the features related to diagnosis during the denoising process. PURPOSE To provide a training strategy for LDCT denoisers that relies more on diagnostic task-related features to improve diagnostic accuracy. METHODS An attentive map derived from a lesion classifier (i.e., determining lesion-present or not) is created to represent the extent to which each pixel influences the decision by the lesion classifier. This is used as a weight to emphasize important parts of the image. The proposed training method consists of two steps. In the first one, the initial parameters of the CNN denoiser are trained using LDCT and normal-dose CT image pairs via supervised learning. In the second one, the learned parameters are readjusted using the attentive map to restore the fine details of the image. RESULTS Structural details and the contrast are better preserved in images generated by using the denoiser trained via the proposed method than in those generated by conventional denoisers. The proposed denoiser also yields higher lesion detectability and localization accuracy than conventional denoisers. CONCLUSIONS A denoiser trained using the proposed method preserves the small structures and the contrast in the denoised images better than without it. Specifically, using the attentive map improves the lesion detectability and localization accuracy of the denoiser.
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Affiliation(s)
- Minah Han
- Graduate School of Artificial Intelligence, Yonsei University, Seoul, South Korea.,Bareunex Imaging, Inc., Seoul, South Korea
| | - Hyunjung Shim
- Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Jongduk Baek
- Graduate School of Artificial Intelligence, Yonsei University, Seoul, South Korea.,Bareunex Imaging, Inc., Seoul, South Korea
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Zhu L, Han Y, Xi X, Fu H, Tan S, Liu M, Yang S, Liu C, Li L, Yan B. STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT. Med Phys 2023. [PMID: 36708286 DOI: 10.1002/mp.16249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/04/2022] [Accepted: 01/17/2023] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Low-dose computed tomography (LDCT) can reduce the dose of X-ray radiation, making it increasingly significant for routine clinical diagnosis and treatment planning. However, the noise introduced by low-dose X-ray exposure degrades the quality of CT images, affecting the accuracy of clinical diagnosis. Purpose The noises, artifacts, and high-frequency components are similarly distributed in LDCT images. Transformer can capture global context information in an attentional manner to create distant dependencies on targets and extract more powerful features. In this paper, we reduce the impact of image errors on the ability to retain detailed information and improve the noise suppression performance by fully mining the distribution characteristics of image information. METHODS This paper proposed an LDCT noise and artifact suppressing network based on Swin Transformer. The network includes a noise extraction sub-network and a noise removal sub-network. The noise extraction and removal capability are improved using a coarse extraction network of high-frequency features based on full convolution. The noise removal sub-network improves the network's ability to extract relevant image features by using a Swin Transformer with a shift window as an encoder-decoder and skip connections for global feature fusion. Also, the perceptual field is extended by extracting multi-scale features of the images to recover the spatial resolution of the feature maps. The network uses a loss constraint with a combination of L1 and MS-SSIM to improve and ensure the stability and denoising effect of the network. RESULTS The denoising ability and clinical applicability of the methods were tested using clinical datasets. Compared with DnCNN, RED-CNN, CBDNet and TSCN, the STEDNet method shows a better denoising effect on RMSE and PSNR. The STEDNet method effectively removes image noise and preserves the image structure to the maximum extent, making the reconstructed image closest to the NDCT image. The subjective and objective analysis of several sets of experiments shows that the method in this paper can effectively maintain the structure, edges, and textures of the denoised images while having good noise suppression performance. In the real data evaluation, the RMSE of this method is reduced by 18.82%, 15.15%, 2.25%, and 1.10% on average compared with DnCNN, RED-CNN, CBDNet, and TSCNN, respectively. The average improvement of PSNR is 9.53%, 7.33%, 2.65%, and 3.69%, respectively. CONCLUSIONS This paper proposed a LDCT image denoising algorithm based on end-to-end training. The method in this paper can effectively improve the diagnostic performance of CT images by constraining the details of the images and restoring the LDCT image structure. The problem of increased noise and artifacts in CT images can be solved while maintaining the integrity of CT image tissue structure and pathological information. Compared with other algorithms, this method has better denoising effects both quantitatively and qualitatively.
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Affiliation(s)
- Linlin Zhu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Yu Han
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Xiaoqi Xi
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Huijuan Fu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Siyu Tan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Mengnan Liu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Shuangzhan Yang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Chang Liu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.,School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China
| | - Lei Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
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Gao Y, Lu S, Shi Y, Chang S, Zhang H, Hou W, Li L, Liang Z. A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT. Sensors (Basel) 2023; 23:1374. [PMID: 36772417 PMCID: PMC9921255 DOI: 10.3390/s23031374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/18/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes' law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise-resolution tradeoff. The hyperparameter is determined empirically via a trial-and-error fashion in many applications, which then selects the optimal result from multiple iterative reconstructions. These penalized methods are not only time-consuming by their iterative nature, but also require manual adjustment. This study aims to investigate a theory-based strategy for Bayesian image reconstruction without a freely adjustable hyperparameter, to substantially save time and computational resources. The Bayesian image reconstruction problem is formulated by two probability density functions (PDFs), one for the data fidelity term and the other for the prior term. When formulating these PDFs, we introduce two parameters. While these two parameters ensure the PDFs completely describe the data and prior terms, they cannot be determined by the acquired data; thus, they are called complete but unobservable parameters. Estimating these two parameters becomes possible under the conditional expectation and maximization for the image reconstruction, given the acquired data and the PDFs. This leads to an iterative algorithm, which jointly estimates the two parameters and computes the to-be reconstructed image by maximizing a posteriori probability, denoted as joint-parameter-Bayes. In addition to the theoretical formulation, comprehensive simulation experiments are performed to analyze the stopping criterion of the iterative joint-parameter-Bayes method. Finally, given the data, an optimal reconstruction is obtained without any freely adjustable hyperparameter by satisfying the PDF condition for both the data likelihood and the prior probability, and by satisfying the stopping criterion. Moreover, the stability of joint-parameter-Bayes is investigated through factors such as initialization, the PDF specification, and renormalization in an iterative manner. Both phantom simulation and clinical patient data results show that joint-parameter-Bayes can provide comparable reconstructed image quality compared to the conventional methods, but with much less reconstruction time. To see the response of the algorithm to different types of noise, three common noise models are introduced to the simulation data, including white Gaussian noise to post-log sinogram data, Poisson-like signal-dependent noise to post-log sinogram data and Poisson noise to the pre-log transmission data. The experimental outcomes of the white Gaussian noise reveal that the two parameters estimated by the joint-parameter-Bayes method agree well with simulations. It is observed that the parameter introduced to satisfy the prior's PDF is more sensitive to stopping the iteration process for all three noise models. A stability investigation showed that the initial image by filtered back projection is very robust. Clinical patient data demonstrated the effectiveness of the proposed joint-parameter-Bayes and stopping criterion.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Siming Lu
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yongyi Shi
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Shaojie Chang
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Hao Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wei Hou
- Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Lihong Li
- Department of Engineering Science and Physics, CUNY/CSI, Staten Island, NY 10314, USA
| | - Zhengrong Liang
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
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Niehoff JH, Carmichael AF, Woeltjen MM, Boriesosdick J, Michael AE, Schmidt B, Panknin C, Flohr TG, Shahzadi I, Piechota H, Borggrefe J, Kroeger JR. Clinical Low-Dose Photon-Counting CT for the Detection of Urolithiasis: Radiation Dose Reduction Is Possible without Compromising Image Quality. Diagnostics (Basel) 2023; 13:diagnostics13030458. [PMID: 36766563 PMCID: PMC9914353 DOI: 10.3390/diagnostics13030458] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/18/2023] [Accepted: 01/23/2023] [Indexed: 01/28/2023] Open
Abstract
Background: This study evaluated the feasibility of reducing the radiation dose in abdominal imaging of urolithiasis with a clinical photon-counting CT (PCCT) by gradually lowering the image quality level (IQL) without compromising the image quality and diagnostic value. Methods: Ninety-eight PCCT examinations using either IQL70 (n = 31), IQL60 (n = 31) or IQL50 (n = 36) were retrospectively included. Parameters for the radiation dose and the quantitative image quality were analyzed. Qualitative image quality, presence of urolithiasis and diagnostic confidence were rated. Results: Lowering the IQL from 70 to 50 led to a significant decrease (22.8%) in the size-specific dose estimate (SSDE, IQL70 4.57 ± 0.84 mGy, IQL50 3.53 ± 0.70 mGy, p < 0.001). Simultaneously, lowering the IQL led to a minimal deterioration of the quantitative quality, e.g., image noise increased from 9.13 ± 1.99 (IQL70) to 9.91 ± 1.77 (IQL50, p = 0.248). Radiologists did not notice major changes in the image quality throughout the IQLs. Detection rates of urolithiasis (91.3-100%) did not differ markedly. Diagnostic confidence was high and not influenced by the IQL. Conclusions: Adjusting the PCCT scan protocol by lowering the IQL can significantly reduce the radiation dose without significant impairment of the image quality. The detection rate and diagnostic confidence are not impaired by using an ultra-low-dose PCCT scan protocol.
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Affiliation(s)
- Julius Henning Niehoff
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
- Correspondence: ; Tel.: +49-571-790-4601; Fax: +49-571-790-294601
| | - Alexandra Fiona Carmichael
- Department of Urology, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
| | - Matthias Michael Woeltjen
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
| | - Jan Boriesosdick
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
| | - Arwed Elias Michael
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
| | | | | | | | | | - Hansjuergen Piechota
- Department of Urology, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
| | - Jan Borggrefe
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
| | - Jan Robert Kroeger
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany
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Liu Y, Wei C, Xu Q. Detector shifting and deep learning based ring artifact correction method for low-dose CT. Med Phys 2023. [PMID: 36647338 DOI: 10.1002/mp.16225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/13/2022] [Accepted: 01/02/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND In x-ray computed tomography (CT), the gain inconsistency of detector units leads to ring artifacts in the reconstructed images, seriously destroys the image structure, and is not conducive to image recognition. In addition, to reduce radiation dose and scanning time, especially photon counting CT, low-dose CT is required, so it is important to reduce the noise and suppress ring artifacts in low-dose CT images simultaneously. PURPOSE Deep learning is an effective method to suppress ring artifacts, but there are still residual artifacts in corrected images. And the feature recognition ability of the network for ring artifacts decreases due to the effect of noise in the low-dose CT images. In this paper, a method is proposed to achieve noise reduction and ring artifact removal simultaneously. METHODS To solve these problems, we propose a ring artifact correction method for low-dose CT based on detector shifting and deep learning in this paper. Firstly, at the CT scanning stage, the detector horizontally shifts randomly at each projection to alleviate the ring artifacts as front processing. Thus, the ring artifacts are transformed into dispersed noise in front processed images. Secondly, deep learning is used for dispersed noise and statistical noise reduction. RESULTS Both simulation and real data experiments are conducted to evaluate the proposed method. Compared to other methods, the results show that the proposed method in this paper has better effect on removing ring artifacts in the low-dose CT images. Specifically, the RMSEs and SSIMs of the two sets of simulated and experiment data are better compared to the raw images significantly. CONCLUSIONS The method proposed in this paper combines detector shifting and deep learning to remove ring artifacts and statistical noise simultaneously. The results show that the proposed method is able to get better performance.
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Affiliation(s)
- Yuedong Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.,School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Cunfeng Wei
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.,School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China.,Jinan Laboratory of Applied Nuclear Science, Jinan, China
| | - Qiong Xu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.,Jinan Laboratory of Applied Nuclear Science, Jinan, China
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Horáková D, Štěpánek L, Ševčíková J, Durďáková R, Vlčková J. Secondary prevention of lung cancer in the Czech Republic - pitfalls, risks, benefits. Epidemiol Mikrobiol Imunol 2023; 72:120-123. [PMID: 37344225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/23/2023]
Abstract
Lung cancer (LC) is one of the most frequently diagnosed cancers and one of the leading causes of cancer deaths in the Czech Republic, the prevalence of which is steadily increasing. There is scientific evidence that LC screening through low-dose computed tomography (LDCT) reduces the risk of death from LC. No systematic LC screening strategy has been currently in place in the Czech Republic. Since the beginning of 2022, the methodology of early detection of LC using LDCT has been piloted to test the feasibility of the screening program. The primary purpose of the project is an early and accurate diagnosis of the disease, which, in combination with follow-up treatment, will lead to a reduction in LC mortality. The pilot data will definitely serve as a basis for an expert discussion of the acceptability of the program to the Czech population and its impact on the healthcare system. It is clear that by introducing such a screening program, we will join the countries that, based on scientific data, enable the population to profit from an actively implemented LC prevention strategy. Public awareness of the benefits of early non-invasive LC detection can contribute to higher compliance of at-risk persons and their willingness to participate in the program. The key role in the entire process is played by general practitioners and/or outpatient pulmologists who address at-risk individuals and can positively influence their involvement in the program.
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Wang M, Lin S, He N, Yang C, Zhang R, Liu X, Suo C, Lin T, Chen H, Xu W. The Introduction of Low-Dose CT Imaging and Lung Cancer Overdiagnosis in Chinese Women. Chest 2023; 163:239-250. [PMID: 35998705 DOI: 10.1016/j.chest.2022.08.2207] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/27/2022] [Accepted: 08/03/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Overdiagnosis of lung cancer by low-dose CT (LDCT) screening has raised concerns globally. LDCT screening has been used widely in employee health examinations in China since 2011. RESEARCH QUESTION Has the increasing use of LDCT in low-risk populations in China led to lung cancer overdiagnosis? STUDY DESIGN AND METHODS A total of 34,152 incident cases of and 27,208 deaths resulting from lung cancer in a population of approximately 3 million were derived from the Cancer Surveillance of Shanghai between 2002 and 2017. Changes in stage-specific and histologic type-specific incidence and mortality and incidence rate ratio (IRR) relative to the base year 2002 or to the period 2002 through 2005 were calculated by sex and were used to evaluate potential overdiagnosisve of lung cancer. RESULTS In men, both age-adjusted incidence of and mortality as a result of lung cancer decreased significantly up to 2008 and thereafter remained stable; in women, the incidence increased rapidly from 2011 (annual percentage change, 11.98%; 95% CI, 9.57%-14.45%), whereas the mortality declined persistently. The upward trend of incidence mainly was observed in lung adenocarcinoma in both sexes, with a sharper increase from 2012 through 2017. In men, the incidence of early-stage cancer increased 6.9 per 100,000 (95% CI, 5.1-8.7 per 100,000) and was accompanied by 5.5 per 100,000 (95% CI, -9.2 to -1.7 per 100,000) decline in late-stagecancer from 2002 through 2017. In women, early-stage incidence rose 16.1 per 100,000 (95% CI, 14.0-18.3 per 100,000), but no significant decline in late-stage cancer was found (absolute difference, -0.6 per 100,000; 95% CI, -2.8 to 1.7 per 100,000). The IRR was highest in most recent period and increased most in young women, mainly for early-stage cancer or lung adenocarcinoma. INTERPRETATION The results provide evidence at a population level for lung cancer overdiagnosis in Chinese women resulting from increasing LDCT screening in the low-risk populations. Criteria for LDCT screening and management of screening-detected nodules need to be addressed fully for expanded application of LDCT screening in China.
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Affiliation(s)
- Mengyan Wang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Yiwu Research Institute, Fudan University, Yiwu, China
| | - Shangqun Lin
- Centers for Disease Control and Prevention of Pudong New Area, Shanghai, China
| | - Na He
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Yiwu Research Institute, Fudan University, Yiwu, China
| | - Chen Yang
- Centers for Disease Control and Prevention of Pudong New Area, Shanghai, China
| | - Ruoxin Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Yiwu Research Institute, Fudan University, Yiwu, China
| | - Xing Liu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Yiwu Research Institute, Fudan University, Yiwu, China
| | - Chen Suo
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Yiwu Research Institute, Fudan University, Yiwu, China
| | - Tao Lin
- Centers for Disease Control and Prevention of Pudong New Area, Shanghai, China
| | - Haiquan Chen
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wanghong Xu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Yiwu Research Institute, Fudan University, Yiwu, China.
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Zhou S, Yu L, Jin M. Texture transformer super-resolution for low-dose computed tomography. Biomed Phys Eng Express 2022; 8:10.1088/2057-1976/ac9da7. [PMID: 36301699 PMCID: PMC9707552 DOI: 10.1088/2057-1976/ac9da7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022]
Abstract
Computed tomography (CT) is widely used to diagnose many diseases. Low-dose CT has been actively pursued to lower the ionizing radiation risk. A relatively smoother kernel is typically used in low-dose CT to suppress image noise, which may sacrifice spatial resolution. In this work, we propose a texture transformer network to simultaneously reduce image noise and improve spatial resolution in CT images. This network, referred to as Texture Transformer for Super Resolution (TTSR), is a reference-based deep-learning image super-resolution method built upon a generative adversarial network (GAN). The noisy low-resolution CT (LRCT) image and the routine-dose high-resolution (HRCT) image are severed as the query and key in a transformer, respectively. Image translation is optimized through deep neural network (DNN) texture extraction, correlation embedding, and attention-based texture transfer and synthesis to achieve joint feature learning between LRCT and HRCT images for super-resolution CT (SRCT) images. To evaluate SRCT performance, we use the data from both simulations of the XCAT phantom program and the real patient data. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity (FSIM) index are used as quantitative metrics. For comparison of SRCT performance, the cubic spline interpolation, SRGAN (a GAN super-resolution with an additional content loss), and GAN-CIRCLE (a GAN super-resolution with cycle consistency) were used. Compared to the other two methods, TTSR can restore more details in SRCT images and achieve better PSNR, SSIM, and FSIM for both simulation and real-patient data. In addition, we show that TTSR can yield better image quality and demand much less computation time than high-resolution low-dose CT images denoised by block-matching and 3D filtering (BM3D) and GAN-CIRCLE. In summary, the proposed TTSR method based on texture transformer and attention mechanism provides an effective and efficient tool to improve spatial resolution and suppress noise of low-dose CT images.
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Affiliation(s)
- Shiwei Zhou
- Department of Physics, University of Texas at Arlington, TX 76019, United States of America
| | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, United States of America
| | - Mingwu Jin
- Department of Physics, University of Texas at Arlington, TX 76019, United States of America
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Nesteruk KP, Bobić M, Sharp GC, Lalonde A, Winey BA, Nenoff L, Lomax AJ, Paganetti H. Low-Dose Computed Tomography Scanning Protocols for Online Adaptive Proton Therapy of Head-and-Neck Cancers. Cancers (Basel) 2022; 14:cancers14205155. [PMID: 36291939 PMCID: PMC9600085 DOI: 10.3390/cancers14205155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/15/2022] [Accepted: 10/19/2022] [Indexed: 01/11/2023] Open
Abstract
PURPOSE To evaluate the suitability of low-dose CT protocols for online plan adaptation of head-and-neck patients. METHODS We acquired CT scans of a head phantom with protocols corresponding to CT dose index volume CTDIvol in the range of 4.2-165.9 mGy. The highest value corresponds to the standard protocol used for CT simulations of 10 head-and-neck patients included in the study. The minimum value corresponds to the lowest achievable tube current of the GE Discovery RT scanner used for the study. For each patient and each low-dose protocol, the noise relative to the standard protocol, derived from phantom images, was applied to a virtual CT (vCT). The vCT was obtained from a daily CBCT scan corresponding to the fraction with the largest anatomical changes. We ran an established adaptive workflow twice for each low-dose protocol using a high-quality daily vCT and the corresponding low-dose synthetic vCT. For a relative comparison of the adaptation efficacy, two adapted plans were recalculated in the high-quality vCT and evaluated with the contours obtained through deformable registration of the planning CT. We also evaluated the accuracy of dose calculation in low-dose CT volumes using the standard CT protocol as reference. RESULTS The maximum differences in D98 between low-dose protocols and the standard protocol for the high-risk and low-risk CTV were found to be 0.6% and 0.3%, respectively. The difference in OAR sparing was up to 3%. The Dice similarity coefficient between propagated contours obtained with low-dose and standard protocols was above 0.982. The mean 2%/2 mm gamma pass rate for the lowest-dose image, using the standard protocol as reference, was found to be 99.99%. CONCLUSION The differences between low-dose protocols and the standard scanning protocol were marginal. Thus, low-dose CT protocols are suitable for online adaptive proton therapy of head-and-neck cancers. As such, considering scanning protocols used in our clinic, the imaging dose associated with online adaption of head-and-neck cancers treated with protons can be reduced by a factor of 40.
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Affiliation(s)
- Konrad P. Nesteruk
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Correspondence:
| | - Mislav Bobić
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physics, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Gregory C. Sharp
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Arthur Lalonde
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Brian A. Winey
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Lena Nenoff
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Antony J. Lomax
- Department of Physics, ETH Zurich, CH-8093 Zurich, Switzerland
- Center for Proton Therapy, Paul Scherrer Institute, CH-5232 Villigen, Switzerland
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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liang L, zhang H, Lei H, Zhou H, Wu Y, shen J. Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters. Technol Cancer Res Treat 2022; 21:15330338221119748. [PMID: 36259167 PMCID: PMC9583213 DOI: 10.1177/15330338221119748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Objective: To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. Methods: A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Results: Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets (P <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased (P < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a P value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. Conclusion: The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules.
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Affiliation(s)
| | | | | | | | | | - Jiang shen
- Jiang shen, Chongqing Key Laboratory of
Translational Research for Cancer Metastasis and Individualized Treatment,
Chongqing University Cancer Hospital, Chongqing, 400030, China. Emails:
;
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Chung KJ, Khaw AV, Lee DH, Pandey S, Mandzia J, Lee TY. Low-dose CT Perfusion with Sparse-view Filtered Back Projection in Acute Ischemic Stroke. Acad Radiol 2022; 29:1502-1511. [PMID: 35300907 DOI: 10.1016/j.acra.2022.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 01/15/2022] [Accepted: 01/24/2022] [Indexed: 12/30/2022]
Abstract
RATIONALE AND OBJECTIVES Radiation dose associated with computed tomography (CT) perfusion (CTP) may discourage its use despite its added diagnostic benefit in quantifying ischemic lesion volume. Sparse-view CT reduces scan dose by acquiring fewer X-ray projections per gantry rotation but is contaminated by streaking artifacts using filtered back projection (FBP). We investigated the achievable dose reduction by sparse-view CTP with FBP without affecting CTP lesion volume estimations. MATERIALS AND METHODS Thirty-eight consecutive patients with acute ischemic stroke and CTP were included in this simulation study. CTP projection data was simulated by forward projecting original reconstructions with 984 views and adding Gaussian noise. Full-view (984 views) and sparse-view (492, 328, 246, and 164 views) CTP studies were simulated by FBP of simulated projection data. Cerebral blood flow (CBF) and time-to-maximum of the impulse residue function (Tmax) maps were generated by deconvolution for each simulated CTP study. Ischemic volumes were measured by CBF<30% relative to the contralateral hemisphere and Tmax > 6 s. Volume accuracy was evaluated with respect to the full-view CTP study by the Friedman test with post hoc multiplicity-adjusted pairwise tests and Bland-Altman analysis. RESULTS Friedman and multiplicity-adjusted pairwise tests indicated that 164-view CBF < 30%, 246- and 164-view Tmax > 6 s volumes were significantly different to full-view volumes (p < 0.001). Mean difference ± standard deviation (sparse minus full-view lesion volume) ranged from -1.0 ± 2.8 ml to -4.1 ± 11.7 ml for CBF < 30% and -2.9 ± 3.8 ml to -12.5 ± 19.9 ml for Tmax > 6 s from 492 to 164 views, respectively. CONCLUSION By ischemic volume accuracy, our study indicates that sparse-view CTP may allow dose reduction by up to a factor of 3.
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Affiliation(s)
- Kevin J Chung
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada; Robarts Research Institute and Lawson Health Research Institute, University of Western Ontario, 1151 Richmond Street N, London, ON N6A 5B7, Canada
| | - Alexander V Khaw
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Donald H Lee
- Department of Medical Imaging, University of Western Ontario, London, ON, Canada
| | - Sachin Pandey
- Department of Medical Imaging, University of Western Ontario, London, ON, Canada
| | - Jennifer Mandzia
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Ting-Yim Lee
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada; Robarts Research Institute and Lawson Health Research Institute, University of Western Ontario, 1151 Richmond Street N, London, ON N6A 5B7, Canada; Department of Medical Imaging, University of Western Ontario, London, ON, Canada.
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Niehoff JH, Carmichael AF, Woeltjen MM, Boriesosdick J, Lopez Schmidt I, Michael AE, Große Hokamp N, Piechota H, Borggrefe J, Kroeger JR. Clinical Low Dose Photon Counting CT for the Detection of Urolithiasis: Evaluation of Image Quality and Radiation Dose. Tomography 2022; 8:1666-1675. [PMID: 35894003 PMCID: PMC9326560 DOI: 10.3390/tomography8040138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 11/18/2022] Open
Abstract
The purpose of this study was the evaluation of image quality and radiation dose parameters of the novel photon counting CT (PCCT, Naeotom Alpha, Siemens Healthineers) using low-dose scan protocols for the detection of urolithiasis. Standard CT scans were used as a reference (S40, Somatom Sensation 40, Siemens Healthineers). Sixty-three patients, who underwent CT scans between August and December 2021, were retrospectively enrolled. Thirty-one patients were examined with the PCCT and 32 patients were examined with the S40. Radiation dose parameters, as well as quantitative and qualitative image parameters, were analyzed. The presence of urolithiasis, image quality, and diagnostic certainty were rated on a 5-point-scale by 3 blinded readers. Both patient groups (PCCT and S40) did not differ significantly in terms of body mass index. Radiation dose was significantly lower for examinations with the PCCT compared to the S40 (2.4 ± 1.0 mSv vs. 3.4 ± 1.0 mSv; p < 0.001). The SNR was significantly better on images acquired with the PCCT (13.3 ± 3.3 vs. 8.2 ± 1.9; p < 0.001). The image quality of the PCCT was rated significantly better (4.3 ± 0.7 vs. 2.8 ± 0.6; p < 0.001). The detection rate of kidney or ureter calculi was excellent with both CT scanners (PCCT 97.8% and S40 99%, p = 0.611). In high contrast imaging, such as the depiction of stones of the kidney and the ureter, PCCT allows a significant reduction of radiation dose, while maintaining excellent diagnostic confidence and image quality. Given this image quality with our current protocol, further adjustments towards ultra-low-dose CT scans appear feasible.
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Affiliation(s)
- Julius Henning Niehoff
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
- Correspondence: ; Tel.: +49-571-790-4601
| | - Alexandra Fiona Carmichael
- Department of Urology, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (A.F.C.); (H.P.)
| | - Matthias Michael Woeltjen
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
| | - Jan Boriesosdick
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
| | - Ingo Lopez Schmidt
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
| | - Arwed Elias Michael
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
| | - Nils Große Hokamp
- Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, 50937 Cologne, Germany;
| | - Hansjuergen Piechota
- Department of Urology, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (A.F.C.); (H.P.)
| | - Jan Borggrefe
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
| | - Jan Robert Kroeger
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 44801 Bochum, Germany; (M.M.W.); (J.B.); (I.L.S.); (A.E.M.); (J.B.); (J.R.K.)
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Liu P, Xu L, Fullerton G, Xiao Y, Nguyen JB, Li Z, Barreto I, Olguin C, Fang R. PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra- Low-Dose CT Restoration. Front Radiol 2022; 2:904601. [PMID: 37492656 PMCID: PMC10365089 DOI: 10.3389/fradi.2022.904601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/22/2022] [Indexed: 07/27/2023]
Abstract
A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Linsong Xu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Garrett Fullerton
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Yao Xiao
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - James-Bond Nguyen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Zhongyu Li
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Izabella Barreto
- Department of Radiology, University of Florida, Gainesville, FL, United States
| | - Catherine Olguin
- Department of Radiology, University of Florida, Gainesville, FL, United States
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
- Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States
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Li C, Wang H, Jiang Y, Fu W, Liu X, Zhong R, Cheng B, Zhu F, Xiang Y, He J, Liang W. Advances in lung cancer screening and early detection. Cancer Biol Med 2022; 19:j.issn.2095-3941.2021.0690. [PMID: 35535966 PMCID: PMC9196057 DOI: 10.20892/j.issn.2095-3941.2021.0690] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/03/2022] [Indexed: 11/18/2022] Open
Abstract
Lung cancer is associated with a heavy cancer-related burden in terms of patients' physical and mental health worldwide. Two randomized controlled trials, the US-National Lung Screening Trial (NLST) and Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON), indicated that low-dose CT (LDCT) screening results in a statistically significant decrease in mortality in patients with lung cancer, LDCT has become the standard approach for lung cancer screening. However, many issues in lung cancer screening remain unresolved, such as the screening criteria, high false-positive rate, and radiation exposure. This review first summarizes recent studies on lung cancer screening from the US, Europe, and Asia, and discusses risk-based selection for screening and the related issues. Second, an overview of novel techniques for the differential diagnosis of pulmonary nodules, including artificial intelligence and molecular biomarker-based screening, is presented. Third, current explorations of strategies for suspected malignancy are summarized. Overall, this review aims to help clinicians understand recent progress in lung cancer screening and alleviate the burden of lung cancer.
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Affiliation(s)
- Caichen Li
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Huiting Wang
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Yu Jiang
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Wenhai Fu
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
| | - Xiwen Liu
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Ran Zhong
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Bo Cheng
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Feng Zhu
- Department of Internal Medicine, Detroit Medical Center Sinai-Grace Hospital, Detroit, Michigan 48235, USA
| | - Yang Xiang
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
| | - Jianxing He
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
- Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou 510515, China
| | - Wenhua Liang
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
- Department of Oncology, the First People’s Hospital of Zhaoqing, Zhaoqing 526020, China
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Wang PH, Gow CH, Chiu YL, Li TC. Determination of Low Muscle Mass by Muscle Surface Index of the First Lumbar Vertebra Using Low-Dose Computed Tomography. J Clin Med 2022; 11:jcm11092429. [PMID: 35566554 PMCID: PMC9103630 DOI: 10.3390/jcm11092429] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
The muscle index of the first vertebra (L1MI) derived from computed tomography (CT) is an indicator of total skeletal muscle mass. Nevertheless, the cutoff value and utility of L1MI derived from low-dose chest CT (LDCT) remain unclear. Adults who received LDCT for health check-ups in 2017 were enrolled. The cutoff values of L1MI were established in subjects aged 20-60 years. The cutoff values were used in chronic obstructive pulmonary disease (COPD) patients to determine muscle quantity. A total of 1780 healthy subjects were enrolled. Subjects (n = 1393) aged 20-60 years were defined as the reference group. The sex-specific cutoff values of L1MI were 26.2 cm2/m2 for males and 20.9 cm2/m2 for females. Six subjects in the COPD group (6/44, 13.6%) had low L1MI. COPD subjects with low L1MI had lower forced expiratory volume in one second (0.81 ± 0.17 vs. 1.30 ± 0.55 L/s, p = 0.046) and higher COPD assessment test scores (19.5 ± 2.6 vs. 15.0 ± 4.9, p = 0.015) than those with normal L1MI. In conclusion, LDCT in health assessments may provide additional information on sarcopenia.
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Affiliation(s)
- Ping-Huai Wang
- Division of Pulmonology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan;
- Department of Nursing, Asia Eastern University of Science and Technology, New Taipei City 220, Taiwan
| | - Chien-Hung Gow
- Division of Pulmonology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan;
- Department of Healthcare Information and Management, Ming-Chuan University, Taoyuan 333, Taiwan
- Correspondence: ; Tel.: +886-2-772815
| | - Yen-Ling Chiu
- Graduate Institute of Medicine and Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan 320, Taiwan;
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan
- Graduate Institute of Clinical Medicine, National Taiwan University, Taipei 100, Taiwan
| | - Tien-Chi Li
- Department of Radiology, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan;
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Pinsky PF, Lynch DA, Gierada DS. Incidental Findings on Low-Dose CT Scan Lung Cancer Screenings and Deaths From Respiratory Diseases. Chest 2022; 161:1092-1100. [PMID: 34838524 PMCID: PMC9005861 DOI: 10.1016/j.chest.2021.11.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Incidental respiratory disease-related findings are frequently observed on low-dose CT (LDCT) lung cancer screenings. This study analyzed data from the National Lung Screening Trial (NLST) to assess the relationship between such findings and respiratory disease mortality (RDM), excluding lung cancer. RESEARCH QUESTION Are incidental respiratory findings on LDCT scanning associated with increased RDM? STUDY DESIGN AND METHODS Subjects in the NLST LDCT arm received three annual screens. Trial radiologists noted findings related to possible lung cancer, as well as respiratory-related incidental findings. Demographic characteristics, smoking history, and medical history were captured in a baseline questionnaire. Kaplan-Meier curves were used to assess cumulative RDM. Multivariate proportional hazards models were used to assess risk factors for RDM; in addition to incidental CT scan findings, variables included respiratory disease history (COPD/emphysema, and asthma), smoking history, and demographic factors (age, race, sex, and BMI). RESULTS Of 26,722 subjects in the NLST LDCT arm, 25,002 received the baseline screen and a subsequent LDCT screen. Overall, 59% were male, 26.5% were aged ≥ 65 years at baseline, and 10.6% reported a history of COPD/emphysema. Emphysema on LDCT scanning was reported in 30.7% of subjects at baseline and in 44.2% at any screen. Of those with emphysema on baseline LDCT scanning, 18% reported a history of COPD/emphysema. Median mortality follow-up was 10.3 years. There were 3,639 deaths, and 708 were from respiratory diseases. Among subjects with no history of COPD/emphysema, 10-year cumulative RDM ranged from 3.9% for subjects with emphysema and reticular opacities to 1.1% for those with neither condition; the corresponding range among subjects with a COPD/emphysema history was 17.3% (both) to 3.7% (neither). Emphysema on LDCT imaging was associated with a significantly elevated RDM hazard ratio (2.27; 95% CI, 1.92-2.7) in the multivariate model. Reticular opacities (including honeycombing/fibrosis/scar) also had a significantly elevated hazard ratio (1.39; 95% CI, 1.19-1.62). INTERPRETATION Incidental respiratory disease-related findings observed on NLST LDCT screens were frequent and associated with increased mortality from respiratory diseases.
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Affiliation(s)
- Paul F Pinsky
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD.
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO
| | - David S Gierada
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO
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Nekolla EA, Brix G, Griebel J. Lung Cancer Screening with Low-Dose CT: Radiation Risk and Benefit-Risk Assessment for Different Screening Scenarios. Diagnostics (Basel) 2022; 12:diagnostics12020364. [PMID: 35204455 PMCID: PMC8870982 DOI: 10.3390/diagnostics12020364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/23/2022] [Accepted: 01/30/2022] [Indexed: 01/04/2023] Open
Abstract
Lung cancer is a severe disease that affects predominantly smokers and represents a leading cause of cancer death in Europe. Recent meta-analyses of randomized controlled trials (RCTs) have yielded that low-dose computed tomography (LDCT) screening can significantly reduce lung cancer mortality in heavy smokers or ex-smokers by about 20% compared to a control group of persons who did not receive LDCT. This benefit must be weighed against adverse health effects associated with LDCT lung screening, in particular radiation risks. For this purpose, representative organ doses were determined for a volume CT dose index of 1 mGy that can be achieved on modern devices. Using these values, radiation risks were estimated for different screening scenarios by means of sex-, organ-, and age-dependent radio-epidemiologic models. In particular, the approach was adjusted to a Western European population. For an annual LDCT screening of (ex-)smokers aged between 50 and 75 years, the estimated radiation-related lifetime attributable risk to develop cancer is below 0.25% for women and about 0.1% for men. Assuming a mortality reduction of about 20% and taking only radiation risks into account, this screening scenario results in a benefit–risk ratio of about 10 for women and about 25 for men. These benefit–risk ratio estimates are based on the results of RCTs of the highest evidence level. To ensure that the benefit outweighs the radiation risk even in standard healthcare, strict conditions and requirements must be established for the entire screening process to achieve a quality level at least as high as that of the considered RCTs.
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He Y, Liu P, Xie L, Zeng S, Lin H, Zhang B, Liu J. Construction and Verification of a Predictive Model for Risk Factors in Children With Severe Adenoviral Pneumonia. Front Pediatr 2022; 10:874822. [PMID: 35832584 PMCID: PMC9271770 DOI: 10.3389/fped.2022.874822] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To construct and validate a predictive model for risk factors in children with severe adenoviral pneumonia based on chest low-dose CT imaging and clinical features. METHODS A total of 177 patients with adenoviral pneumonia who underwent low-dose CT examination were collected between January 2019 and August 2019. The assessment criteria for severe pneumonia were divided into mild group (N = 125) and severe group (N = 52). All cases divided into training cohort (N = 125) and validation cohort (N = 52). We constructed a prediction model by drawing a nomogram and verified the predictive efficacy of the model through the ROC curve, calibration curve and decision curve analysis. RESULTS The difference was statistically significant (P < 0.05) between the mild adenovirus pneumonia group and the severe adenovirus pneumonia group in gender, age, weight, body temperature, L/N ratio, LDH, ALT, AST, CK-MB, ADV DNA, bronchial inflation sign, emphysema, ground glass sign, bronchial wall thickening, bronchiectasis, pleural effusion, consolidation score, and lobular inflammation score. Multivariate logistic regression analysis showed that gender, LDH value, emphysema, consolidation score, and lobular inflammation score were severe independent risk factors for adenovirus pneumonia in children. Logistic regression was employed to construct clinical model, imaging semantic feature model, and combined model. The AUC values of the training sets of the three models were 0.85 (0.77-0.94), 0.83 (0.75-0.91), and 0.91 (0.85-0.97). The AUC of the validation set was 0.77 (0.64-0.91), 0.83 (0.71-0.94), and 0.85 (0.73-0.96), respectively. The calibration curve fit good of the three models. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model. CONCLUSION The prediction model based on chest low-dose CT image characteristics and clinical characteristics has relatively clear predictive value in distinguishing mild adenovirus pneumonia from severe adenovirus pneumonia in children and might provide a new method for early clinical prediction of the outcome of adenovirus pneumonia in children.
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Affiliation(s)
- Yaqiong He
- Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Leyun Xie
- Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Saizhen Zeng
- Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | | | - Bing Zhang
- Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Jianbin Liu
- Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
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Pierro A, Posa A, Astore C, Sciandra M, Tanzilli A, Petrosino A, del Balso MS, Fraticelli V, Cilla S, Iezzi R. Whole-Body Low-Dose Multidetector-Row CT in Multiple Myeloma: Guidance in Performing, Observing, and Interpreting the Imaging Findings. Life (Basel) 2021; 11:life11121320. [PMID: 34947851 PMCID: PMC8707516 DOI: 10.3390/life11121320] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 11/20/2021] [Accepted: 11/26/2021] [Indexed: 01/21/2023] Open
Abstract
Multiple myeloma is a hematological malignancy of plasma cells usually detected due to various bone abnormalities on imaging and rare extraosseous abnormalities. The traditional approach for disease detection was based on plain radiographs, showing typical lytic lesions. Still, this technique has many limitations in terms of diagnosis and assessment of response to treatment. The new approach to assess osteolytic lesions in patients newly diagnosed with multiple myeloma is based on total-body low-dose CT. The purpose of this paper is to suggest a guide for radiologists in performing and evaluating a total-body low-dose CT in patients with multiple myeloma, both newly-diagnosed and in follow-up (pre and post treatment).
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Affiliation(s)
- Antonio Pierro
- Department of Radiology, “A. Cardarelli” Regional Hospital, ASReM, Contrada Tappino, 86100 Campobasso, Italy; (A.P.); (M.S.); (M.S.d.B.)
| | - Alessandro Posa
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.T.); (A.P.); (R.I.)
- Correspondence:
| | - Costanzo Astore
- Radiology Unit, Gemelli Molise Hospital, L.go A. Gemelli 1, 86100 Campobasso, Italy;
| | - Mariacarmela Sciandra
- Department of Radiology, “A. Cardarelli” Regional Hospital, ASReM, Contrada Tappino, 86100 Campobasso, Italy; (A.P.); (M.S.); (M.S.d.B.)
| | - Alessandro Tanzilli
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.T.); (A.P.); (R.I.)
| | - Antonella Petrosino
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.T.); (A.P.); (R.I.)
| | - Maria Saveria del Balso
- Department of Radiology, “A. Cardarelli” Regional Hospital, ASReM, Contrada Tappino, 86100 Campobasso, Italy; (A.P.); (M.S.); (M.S.d.B.)
| | - Vincenzo Fraticelli
- Hematology Unit, Gemelli Molise Hospital, L.go A. Gemelli 1, 86100 Campobasso, Italy;
| | - Savino Cilla
- Medical Phisics Unit, Gemelli Molise Hospital, L.go A. Gemelli 1, 86100 Campobasso, Italy;
| | - Roberto Iezzi
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.T.); (A.P.); (R.I.)
- Radiology Unit, Gemelli Molise Hospital, L.go A. Gemelli 1, 86100 Campobasso, Italy;
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Qiao EM, Voora RS, Nalawade V, Kotha NV, Qian AS, Nelson TJ, Durkin M, Vitzthum LK, Murphy JD, Stewart TF, Rose BS. Evaluating the clinical trends and benefits of low-dose computed tomography in lung cancer patients. Cancer Med 2021; 10:7289-7297. [PMID: 34528761 PMCID: PMC8525167 DOI: 10.1002/cam4.4229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/30/2021] [Accepted: 07/31/2021] [Indexed: 12/19/2022] Open
Abstract
Background Despite guideline recommendations, utilization of low‐dose computed tomography (LDCT) for lung cancer screening remains low. The driving factors behind these low rates and the real‐world effect of LDCT utilization on lung cancer outcomes remain limited. Methods We identified patients diagnosed with non‐small cell lung cancer (NSCLC) from 2015 to 2017 within the Veterans Health Administration. Multivariable logistic regression assessed the influence of LDCT screening on stage at diagnosis. Lead time correction using published LDCT lead times was performed. Cancer‐specific mortality (CSM) was evaluated using Fine–Gray regression with non‐cancer death as a competing risk. A lasso machine learning model identified important predictors for receiving LDCT screening. Results Among 4664 patients, mean age was 67.8 with 58‐month median follow‐up, 95% CI = [7–71], and 118 patients received ≥1 screening LDCT before NSCLC diagnosis. From 2015 to 2017, LDCT screening increased (0.1%–6.6%, mean = 1.3%). Compared with no screening, patients with ≥1 LDCT were more than twice as likely to present with stage I disease at diagnosis (odds ratio [OR] 2.16 [95% CI 1.46–3.20]) and less than half as likely to present with stage IV (OR 0.38 [CI 0.21–0.70]). Screened patients had lower risk of CSM even after adjusting for LDCT lead time (subdistribution hazard ratio 0.60 [CI 0.42–0.85]). The machine learning model achieved an area under curve of 0.87 and identified diagnosis year and region as the most important predictors for receiving LDCT. White, non‐Hispanic patients were more likely to receive LDCT screening, whereas minority, older, female, and unemployed patients were less likely. Conclusions Utilization of LDCT screening is increasing, although remains low. Consistent with randomized data, LDCT‐screened patients were diagnosed at earlier stages and had lower CSM. LDCT availability appeared to be the main predictor of utilization. Providing access to more patients, including those in diverse racial and socioeconomic groups, should be a priority.
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Affiliation(s)
- Edmund M Qiao
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Rohith S Voora
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Vinit Nalawade
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Nikhil V Kotha
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Alexander S Qian
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Tyler J Nelson
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Michael Durkin
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Lucas K Vitzthum
- Department of Radiation Oncology, Stanford University, Stanford, California, USA
| | - James D Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
| | - Tyler F Stewart
- Division of Hematology-Oncology, Department of Internal Medicine, University of California San Diego, La Jolla, California, USA
| | - Brent S Rose
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Veterans Health Administration San Diego Health Care System, La Jolla, California, USA
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Yang X, Long Y, Ravishankar S. Multilayer residual sparsifying transform (MARS) model for low-dose CT image reconstruction. Med Phys 2021; 48:6388-6400. [PMID: 34514587 DOI: 10.1002/mp.15013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 05/14/2021] [Accepted: 05/19/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multilayer model learned in an unsupervised manner by combining both sparse representations and deep models. The proposed framework extends the classical sparsifying transform model for images to a Multilayer residual sparsifying transform (MARS) model, wherein the transform domain data are jointly sparsified over layers. We investigate the application of MARS models learned from limited regular-dose images for low-dose CT reconstruction using penalized weighted least squares (PWLS) optimization. METHODS We propose new formulations for multilayer transform learning and image reconstruction. We derive an efficient block coordinate descent algorithm to learn the transforms across layers, in an unsupervised manner from limited regular-dose images. The learned model is then incorporated into the low-dose image reconstruction phase. RESULTS Low-dose CT experimental results with both the XCAT phantom and Mayo Clinic data show that the MARS model outperforms conventional methods such as filtered back-projection and PWLS methods based on the edge-preserving (EP) regularizer in terms of two numerical metrics (RMSE and SSIM) and noise suppression. Compared with the single-layer learned transform (ST) model, the MARS model performs better in maintaining some subtle details. CONCLUSIONS This work presents a novel data-driven regularization framework for CT image reconstruction that exploits learned multilayer or cascaded residual sparsifying transforms. The image model is learned in an unsupervised manner from limited images. Our experimental results demonstrate the promising performance of the proposed multilayer scheme over single-layer learned sparsifying transforms. Learned MARS models also offer better image quality than typical nonadaptive PWLS methods.
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Affiliation(s)
- Xikai Yang
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yong Long
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Saiprasad Ravishankar
- Department of Computational Mathematics, Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
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Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021; 48:5727-5742. [PMID: 34387360 DOI: 10.1002/mp.15161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/09/2021] [Accepted: 08/08/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Convolutional neural network (CNN)-based denoising is an effective method for reducing complex computed tomography (CT) noise. However, the image blur induced by denoising processes is a major concern. The main source of image blur is the pixel-level loss (e.g., mean squared error [MSE] and mean absolute error [MAE]) used to train a CNN denoiser. To reduce the image blur, feature-level loss is utilized to train a CNN denoiser. A CNN denoiser trained using visual geometry group (VGG) loss can preserve the small structures, edges, and texture of the image.However, VGG loss, derived from an ImageNet-pretrained image classifier, is not optimal for training a CNN denoiser for CT images. ImageNet contains natural RGB images, so the features extracted by the ImageNet-pretrained model cannot represent the characteristics of CT images that are highly correlated with diagnosis. Furthermore, a CNN denoiser trained with VGG loss causes bias in CT number. Therefore, we propose to use a binary classification network trained using CT images as a feature extractor and newly define the feature-level loss as observer loss. METHODS As obtaining labeled CT images for training classification network is difficult, we create labels by inserting simulated lesions. We conduct two separate classification tasks, signal-known-exactly (SKE) and signal-known-statistically (SKS), and define the corresponding feature-level losses as SKE loss and SKS loss, respectively. We use SKE loss and SKS loss to train CNN denoiser. RESULTS Compared to pixel-level losses, a CNN denoiser trained using observer loss (i.e., SKE loss and SKS loss) is effective in preserving structure, edge, and texture. Observer loss also resolves the bias in CT number, which is a problem of VGG loss. Comparing observer losses using SKE and SKS tasks, SKS yields images having a more similar noise structure to reference images. CONCLUSIONS Using observer loss for training CNN denoiser is effective to preserve structure, edge, and texture in denoised images and prevent the CT number bias. In particular, when using SKS loss, denoised images having a similar noise structure to reference images are generated.
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Affiliation(s)
- Minah Han
- School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea
| | - Hyunjung Shim
- School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea
| | - Jongduk Baek
- School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea
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Zhang Z, Liang X, Zhao W, Xing L. Noise2Context: Context-assisted learning 3D thin-layer for low-dose CT. Med Phys 2021; 48:5794-5803. [PMID: 34287948 DOI: 10.1002/mp.15119] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/31/2021] [Accepted: 07/08/2021] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of x-ray radiation exposure attract more and more attention. To lower the x-ray radiation, low-dose CT (LDCT) has been widely adopted in certain scenarios, while it will induce the degradation of CT image quality. In this paper, we proposed a deep learning-based method that can train denoising neural networks without any clean data. METHODS In this work, for 3D thin-slice LDCT scanning, we first drive an unsupervised loss function which was equivalent to a supervised loss function with paired noisy and clean samples when the noise in the different slices from a single scan was uncorrelated and zero-mean. Then, we trained the denoising neural network to map one noise LDCT image to its two adjacent LDCT images in a single 3D thin-layer LDCT scanning, simultaneously. In essence, with some latent assumptions, we proposed an unsupervised loss function to train the denoising neural network in an unsupervised manner, which integrated the similarity between adjacent CT slices in 3D thin-layer LDCT. RESULTS Further experiments on Mayo LDCT dataset and a realistic pig head were carried out. In the experiments using Mayo LDCT dataset, our unsupervised method can obtain performance comparable to that of the supervised baseline. With the realistic pig head, our method can achieve optimal performance at different noise levels as compared to all the other methods that demonstrated the superiority and robustness of the proposed Noise2Context. CONCLUSIONS In this work, we present a generalizable LDCT image denoising method without any clean data. As a result, our method not only gets rid of the complex artificial image priors but also amounts of paired high-quality training datasets.
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Affiliation(s)
- Zhicheng Zhang
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Xiaokun Liang
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Wei Zhao
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
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Hunger T, Wanka-Pail E, Brix G, Griebel J. Lung Cancer Screening with Low-Dose CT in Smokers: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2021; 11:diagnostics11061040. [PMID: 34198856 PMCID: PMC8228723 DOI: 10.3390/diagnostics11061040] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/21/2021] [Accepted: 06/01/2021] [Indexed: 02/06/2023] Open
Abstract
Lung cancer continues to be one of the main causes of cancer death in Europe. Low-dose computed tomography (LDCT) has shown high potential for screening of lung cancer in smokers, most recently in two European trials. The aim of this review was to assess lung cancer screening of smokers by LDCT with respect to clinical effectiveness, radiological procedures, quality of life, and changes in smoking behavior. We searched electronic databases in April 2020 for publications of randomized controlled trials (RCT) reporting on lung cancer and overall mortality, lung cancer morbidity, and harms of LDCT screening. A meta-analysis was performed to estimate effects on mortality. Forty-three publications on 10 RCTs were included. The meta-analysis of eight studies showed a statistically significant relative reduction of lung cancer mortality of 12% in the screening group (risk ratio = 0.88; 95% CI: 0.79-0.97). Between 4% and 24% of screening-LDCT scans were classified as positive, and 84-96% of them turned out to be false positive. The risk of overdiagnosis was estimated between 19% and 69% of diagnosed lung cancers. Lung cancer screening can reduce disease-specific mortality in (former) smokers when stringent requirements and quality standards for performance are met.
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Sheahan KP, Glynn D, Joyce S, Maher MM, Boland F, O'Connor OJ. Best Practices: Imaging Strategies for Reduced-Dose Chest CT in the Management of Cystic Fibrosis-Related Lung Disease. AJR Am J Roentgenol 2021; 217:304-13. [PMID: 34076456 DOI: 10.2214/AJR.19.22694] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE. Cystic fibrosis (CF) is a multisystemic life-limiting disorder. The leading cause of morbidity in CF is chronic pulmonary disease. Chest CT is the reference standard for detection of bronchiectasis. Cumulative ionizing radiation limits the use of CT, particularly as treatments improve and life expectancy increases. The purpose of this article is to summarize the evidence on low-dose chest CT and its effect on image quality to determine best practices for imaging in CF. CONCLUSION. Low-dose chest CT is technically feasible, reduces dose, and renders satisfactory image quality. There are few comparison studies of low-dose chest CT and standard chest CT in CF; however, evidence suggests equivalent diagnostic capability. Low-dose chest CT with iterative reconstructive algorithms appears superior to chest radiography and equivalent to standard CT and has potential for early detection of bronchiectasis and infective exacerbations, because clinically significant abnormalities can develop in patients who do not have symptoms. Infection and inflammation remain the primary causes of morbidity requiring early intervention. Research gaps include the benefits of replacing chest radiography with low-dose chest CT in terms of improved diagnostic yield, clinical decision making, and patient outcomes. Longitudinal clinical studies comparing CT with MRI for the monitoring of CF lung disease may better establish the complementary strengths of these imaging modalities.
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Finance J, Zieleskewicz L, Habert P, Jacquier A, Parola P, Boussuges A, Bregeon F, Eldin C. Low Dose Chest CT and Lung Ultrasound for the Diagnosis and Management of COVID-19. J Clin Med 2021; 10:jcm10102196. [PMID: 34069557 PMCID: PMC8160936 DOI: 10.3390/jcm10102196] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/13/2021] [Accepted: 05/17/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has provided an opportunity to use low- and non-radiating chest imaging techniques on a large scale in the context of an infectious disease, which has never been done before. Previously, low-dose techniques were rarely used for infectious diseases, despite the recognised danger of ionising radiation. METHOD To evaluate the role of low-dose computed tomography (LDCT) and lung ultrasound (LUS) in managing COVID-19 pneumonia, we performed a review of the literature including our cases. RESULTS Chest LDCT is now performed routinely when diagnosing and assessing the severity of COVID-19, allowing patients to be rapidly triaged. The extent of lung involvement assessed by LDCT is accurate in terms of predicting poor clinical outcomes in COVID-19-infected patients. Infectious disease specialists are less familiar with LUS, but this technique is also of great interest for a rapid diagnosis of patients with COVID-19 and is effective at assessing patient prognosis. CONCLUSIONS COVID-19 is currently accelerating the transition to low-dose and "no-dose" imaging techniques to explore infectious pneumonia and their long-term consequences.
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Affiliation(s)
- Julie Finance
- IRD, APHM, MEPHI, IHU Méditerranée Infection, Aix Marseille University, 13005 Marseille, France; (J.F.); (F.B.)
- Service des Explorations Fonctionnelles Respiratoires, APHM, 13005 Marseille, France
| | - Laurent Zieleskewicz
- Department of Anaesthesiology and Intensive Care Medicine, Hôpital Nord, APHM, Aix Marseille Université, 13005 Marseille, France;
- INRA, INSERM, Centre for Cardiovascular and Nutrition Research (C2VN), Aix Marseille Université, 13005 Marseille, France;
| | - Paul Habert
- Service de Radiologie Cardio-Thoracique, Hôpital La Timone, APHM, 13005 Marseille, France; (P.H.); (A.J.)
- LIIE, Aix Marseille University, 13005 Marseille, France
| | - Alexis Jacquier
- Service de Radiologie Cardio-Thoracique, Hôpital La Timone, APHM, 13005 Marseille, France; (P.H.); (A.J.)
- CNRS, CRMBM-CEMEREM (Centre de Résonance Magnétique Biologique et Médicale—Centre d’Exploration Métaboliques par Résonance Magnétique), APHM, Aix-Marseille University, UMR 7339, 13005 Marseille, France
| | - Philippe Parola
- IRD, APHM, SSA, VITROME, Aix Marseille University, 13005 Marseille, France;
- IHU-Méditerranée Infection, Aix Marseille University, 13005 Marseille, France
| | - Alain Boussuges
- INRA, INSERM, Centre for Cardiovascular and Nutrition Research (C2VN), Aix Marseille Université, 13005 Marseille, France;
| | - Fabienne Bregeon
- IRD, APHM, MEPHI, IHU Méditerranée Infection, Aix Marseille University, 13005 Marseille, France; (J.F.); (F.B.)
- Service des Explorations Fonctionnelles Respiratoires, APHM, 13005 Marseille, France
| | - Carole Eldin
- IRD, APHM, SSA, VITROME, Aix Marseille University, 13005 Marseille, France;
- IHU-Méditerranée Infection, Aix Marseille University, 13005 Marseille, France
- Correspondence:
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Abstract
Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.
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Affiliation(s)
- Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Roberta E Ledda
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ugo Pastorino
- Section of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milano, Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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Li M, Du Q, Duan L, Yang X, Zheng J, Jiang H, Li M. Incorporation of residual attention modules into two neural networks for low-dose CT denoising. Med Phys 2021; 48:2973-2990. [PMID: 33890681 DOI: 10.1002/mp.14856] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 01/06/2021] [Accepted: 03/08/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The low-dose computed tomography (CT) imaging can reduce the damage caused by x-ray radiation to the human body. However, low-dose CT images have a different degree of artifacts than conventional CT images, and their resolution is lower than that of conventional CT images, which can affect disease diagnosis by clinicians. Therefore, methods for noise-level reduction and resolution improvement in low-dose CT images have inevitably become a research hotspot in the field of low-dose CT imaging. METHODS In this paper, residual attention modules (RAMs) are incorporated into the residual encoder-decoder convolutional neural network (RED-CNN) and generative adversarial network with Wasserstein distance (WGAN) to learn features that are beneficial to improving the performances of denoising networks, and developed models are denoted as RED-CNN-RAM and WGAN-RAM, respectively. In detail, RAM is composed of a multi-scale convolution module and an attention module built on the residual network architecture, where the attention module consists of a channel attention module and a spatial attention module. The residual network architecture solves the problem of network degradation with increased network depth. The function of the attention module is to learn which features are beneficial to reduce the noise level of low-dose CT images to reduce the loss of detail in the final denoising images, which is also the key point of the proposed algorithms. RESULTS To develop a robust network for low-dose CT image denoising, multidose-level torso phantom images provided by a cooperating equipment vendor are used to train the network, which can improve the network's adaptability to clinical application. In addition, a clinical dataset is used to test the network's migration capabilities and clinical applicability. The experimental results demonstrate that these proposed networks can effectively remove noise and artifacts from multidose CT scans. Subjective and objective analyses of multiple groups of comparison experiments show that the proposed networks achieve good noise suppression performance while preserving the image texture details. CONCLUSION In this study, two deep learning network models are developed using multidose-level CT images acquired from a commercial spiral CT scanner. The two network models can reduce and even remove streaking artifacts, and noise from low-dose CT images confirms the effectiveness of the proposed algorithms.
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Affiliation(s)
- Mei Li
- Changchun University of Science and Technology, Changchun, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Qiang Du
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Luwen Duan
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jian Zheng
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Haochuan Jiang
- Minfound Medical Systems Co. Ltd., Yuecheng District, Shaoxing, Zhejiang, China
| | - Ming Li
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
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